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Guo, L.; Yuan, X.; , .; Zhou, X. Target Detection and Recognition in Turbid Waters. Encyclopedia. Available online: (accessed on 01 December 2023).
Guo L, Yuan X,  , Zhou X. Target Detection and Recognition in Turbid Waters. Encyclopedia. Available at: Accessed December 01, 2023.
Guo, Linxu, Xin Yuan,  , Xiaoteng Zhou. "Target Detection and Recognition in Turbid Waters" Encyclopedia, (accessed December 01, 2023).
Guo, L., Yuan, X., , ., & Zhou, X.(2022, May 20). Target Detection and Recognition in Turbid Waters. In Encyclopedia.
Guo, Linxu, et al. "Target Detection and Recognition in Turbid Waters." Encyclopedia. Web. 20 May, 2022.
Target Detection and Recognition in Turbid Waters

Turbid water can be divided into shallow turbid water and two kinds of deep turbid water. Shallow turbid water, such as turbid fish farms, has a significant impact on the transmission of light information due to the high density of aquatic organisms and suspended matters, such as fishes and sediments, in the water. This leads to significant image distortion, such as blurred target features, severe distortion, and color changes, which pose a significant challenge regarding visual technology and target recognition. Deep turbid water, such as some deep water areas, is challenged by the same problems as shallow turbid water and has low light conditions, resulting in the instrument receiving limited effective target light information. Due to these factors, traditional target detection and recognition methods cannot meet instruments’ technical requirements. Therefore, the investigation of object detection and recognition in turbid areas is necessary. Currently, research on underwater vision is mainly focusing on scenarios with good water conditions, such as experimental pools, lakes, inland rivers, etc. Due to the complexity of underwater environments, significant differences between various types of water exist. As large-scale engineering operations are often carried out at sea, most research methods do not contain adequate robustness to overcome the significant difficulties encountered in practical engineering applications.

turbid water underwater operation image processing

1. Target Detection Based on Deep Learning Methods

In recent years, deep learning technology has been widely used in underwater image defogging and target identification. Methods based on deep learning investigate image sets by training the neural network and seek to establish a logical relationship to improve the image clarity or extract target features for intelligent recognition. In turbid waters, such as typical fish farms, monitoring of fish in real-time is required. Therefore, accurate identification of targets and prediction of motion trajectories in turbid waters is important. Due to the typical problems, such as low contrast, a lack of inherent features, blurring, and distortion, caused by the turbidity of water or the presence of suspended particles in water in aquaculture farms, target detection of fish is difficult.
Traditional target detection methods manually extract the features of target areas, which is time consuming and has poor robustness. With the appearance of the deep learning convolution neural network, the target detection algorithm entered a new stage. Existing target detection algorithms are mainly divided into two-stage algorithms and one-stage algorithms. Two-stage algorithms mainly include the RCNN series algorithms (RCNN [1], Fast RCNN [2], Faster RCNN [3]). These algorithms first generate regional proposals, and then perform classification and regression tasks on the regional proposals. Thus, detection is improved, but the processing time is increased accordingly. Such algorithms are more suitable for the detection of static underwater objects, such as seafloor rocks, corals, etc. Single-stage algorithms mainly include the SSD [4] algorithm and YOLO series algorithms (YOLO [5], YOLOv2 [6], YOLOv3 [7]). These algorithms improve the detection speed and maintain the detection effect as much as possible and use the direct regression method to forecast the category and location of targets. Therefore, such an algorithm is suitable for when the target detection required is frequent due to frequent aquatic activities, such as those of fish.
Effective target feature detectors and classifiers provide deep learning methods with an advantage in turbid water environments, which is why it is important for computers to adapt the fuzzy characteristic of turbid water and precisely identify target features. Lakshmi and Santhanam [8] proposed two types of classifiers: one is a two-classification convolution neural network for distinguishing between the target and background while the other is a multi-classification convolution neural network for predicting the background or one type of target, such as shoes and ropes. They trained a convolution neural network (CNN) to classify 64 × 64 inputs. The images were classified, and the classifier was used as the target feature detector. The accuracy rate of the 2-class convolution neural network was 93.9%, and the target detection accuracy rate of the multi-class convolution neural network was 90.1%, which is higher than existing multi-class detectors (88%).
Liu et al. [9] combined image processing with deep learning to realize species identification and density calculation of marine organisms to monitor the invasion of marine organisms in real-time. An underwater camera was used to capture image data in real-time within the monitoring range and deep learning was used to achieve end-to-end recognition of jellyfish. First, the convolution neural network was designed and improved, and a convolution neural network composed of two convolution layers, two pooling layers, and a full connection layer was obtained. After training, the convolution neural network was predicted using test sample images. Under the non-uniform light field, the characteristics of the biological images taken in turbid water were investigated using image sharpening, edge detection, edge closure, hole filling, etc. A binary image separate from the target and background was obtained, demonstrating real-time estimation of the marine biological density. The results showed that this method can be effectively applied to calculate the marine biological density and detect marine biological species.
When the Laplace method was used, the accuracy was 68%. The accuracy of SobelX was similar to that of SobelY. Canny showed higher accuracy than the other methods and has been widely used in edge detection. Therefore, the Canny edge detection method is the best algorithm for detecting the edge of the target contour in a turbid area.
In practical engineering applications, an algorithm that can identify the underwater bios trajectory of fisheries to enable easier localization and capture has also been investigated. As an optimization tool, the genetic algorithm has been widely used in various fields, but it has not been fully studied in terms of the trajectory prediction of moving targets. However, the concept of the dynamic traveler problem based on the genetic algorithm and Newton equation of motion was used to obtain excellent results in predicting the minimum distance traveled by a moving fishing boat in the future. Since use of the genetic algorithm (GA) in this field has not been fully realized, Palconit et al. [10] further discussed its application potential in fish tracking based on GA. On the other hand, the deep learning algorithms recurrent neural network (RNN) and long short-term memory (LSTM) have been used in several visual track prediction methods to predict targets, including pedestrians, vehicles, mobile robots, fish, etc. The results from these methods were shown to be better than most tracking methods, and thereby underwater video fish tracking research has been carried out based on RNN-LSTM. The results showed that trajectory prediction using LSTM is more accurate than the use of a genetic algorithm, but both showed an acceptable accuracy and the average absolute percentage errors of GA and LSTM were 2.8~30.5% and 3.33~17.74%, respectively. LSTM has been widely used in trajectory prediction in many fields while the genetic algorithm has seldom been used as a trajectory prediction method. The results of GA can be improved through the use of additional variables or fitness functions, such as Newton’s equation of motion and quadratic regression. Three-dimensional coordinates have been shown to provide more accurate prediction results for GA and LSTM, so it can be further extended for two-dimensional and three-dimensional path prediction in the future, such as the use of GA and LSTM in fish tracking and marking or investigation of its combination with other tracking algorithms.
Intelligent target recognition and positioning using deep learning methods is powerful. However, the accuracy of underwater target recognition is affected by the image clarity, and deep learning methods are only applicable in waters that are similar to the training set image, so this method has some limitations. Therefore, the combination of good image restoration methods and deep learning methods can make target detection and recognition in turbid waters more effective.

2. Underwater Image Restoration and Enhancement Methods

Deep learning has been widely used in underwater image restoration and enhancement to improve the quality of underwater images to a certain extent. Methods based on deep learning can be used to study the relationship between the features of an image set by training the neural network, and reduce the error caused by prior invalidity. Some characteristics of turbid water are similar to those of foggy weather, including problems regarding the attenuation of reflected light, blur caused by tiny impurities, and abnormal changes in color. These factors result in severe color distortion and low visibility [11] in the captured image, so suitable light models and algorithms need to be developed to eliminate any negative impacts. Because underwater image processing and defogging have certain similarities, various defogging algorithms have been gradually improved for application to the enhancement of underwater images.
Thomas et al. [12] developed a fully connected convolution neural network for underwater image defogging. The integration of low-level and high-level features through the depth frame of the encoder-decoder helped to restore blurred images, showing better results than existing methods, such as the structural similarity index (SSIM) [13], peak signal to noise ratio (PSNR), and mean square error (MSE). It was also able to retain details during the removal of fog. Dudhane et al. [14] proposed an end-to-end trainable image defogging network called LIGHT-Net, which includes a color constancy module and a haze reduction module. The color constancy module was used to remove color differences in the image caused by the weather conditions, and the haze reduction module used an initial residual module to reduce the haze effect. Feature sharing was also proposed in this module, which means the features learned at the initial level are effectively shared through the network. The experimental results of this method are promising. Yin and Ma [15] proposed a migration learning method for several types of naturally degraded image enhancement, including underwater image enhancement. They used transfer learning for each specific natural degradation. By repeatedly applying the general enhancement model, they overcame existing problems regarding the shortage of training datasets for in-depth learning methods and the computational burden of the training process. The enhanced model was finetuned, and its performance surpassed several of the most advanced methods designed for specific tasks, such as uwcnn [16] and funie-gan [17].
On this basis, a method for fish detection in restored images using CNN was proposed, and a PC-based automatic target detection recognition visual system was developed. The training results of CNN were also shown to be significantly better than that of the traditional model, which solves the problem of inaccurate target detection caused by blurred images.
Cecilia et al. [18] proposed an effective edge perception restoration and enhancement model for severely blurred shallow coastal images with low contrast. Restoration methods, which are based on the dark channel and rolling guidance filter, were used to restore and denoise such images, resulting in clearer edge perception. This method introduced a rolling guidance filter in dark channel prior (DCP) restoration, which effectively restored images and decreased the noise in the images. This experiment showed that the rolling filter based on the recovery model has better denoising effects.
Regarding the enhancement of the quality of underwater images taken in different water body types, the image forming model used in earlier methods is imprecise and its restoration effect is poor. Zhou et al. [19] developed a defogging method using a modified model. They first designed an underwater image depth estimation method to create depth maps and estimate backscattering based on the depth values of each pixel, and then removed backscattering based on a more accurate underwater imaging model. To address the color distortion characteristics of the turbid area, they proposed a color correction method to automatically adjust the global color distribution of an image. This method used a single underwater image as the input, eliminating the effects of light wave absorption and scattering. Experiments have demonstrated that this approach has better applicability compared with previous research methods.
Considering that scattering attenuation and color correction of high-turbidity underwater images affects the classification results of target recognition based on machine learning (ML), Li et al. [20] proposed a contrast-enhanced method to remove scattering. This enhancement method considers the illumination and camera spectral characteristics, eliminates scattering, and correctly restores the scene color. They also used different ML approaches for classification in their research to confirm that this method can be applied to classification and recognition architecture preprocessing based on deep learning, which showed a better image classification effect. However, for practical applications, use of the scattering removal algorithm does not provide the accuracy required by practical engineering.
Yang [21] proposed an underwater polarized imaging target enhancement technique based on non-polarized illumination to overcome the disadvantages of the current underwater polarized scattering algorithm, such as its low accuracy and limited application range. The use of unpolarized light ensures that any polarization difference between the target reflected light and stray light can be detected. At the same time, the characteristic parameter of the polarization angle ensures accurate estimation of the stray light intensity. Compared with current underwater polarized imaging technology based on linear polarized light illumination, it has a wider application range and higher image restoration accuracy. The results showed that the visibility of underwater restored images is improved effectively, and the contrast is improved by at least 100%. Meanwhile, this technique can be applied to water environments with various material targets, imaging distances, diverse impurities, and turbidity levels, and has potential application value in many underwater imaging fields.
Drews-Jr et al. [22] proposed a new underwater restoration method based on monocular image sequences, which utilizes the time relationship and geometric and environmental information to improve the quality of visual features in underwater images. It can also robustly estimate the depth map and attenuation coefficient. The attenuation coefficient is used to evaluate the loss of light in the medium, so the accuracy of its estimation affects image restoration. Depth estimation is realized using adaptive optical flow and structure motion technology, and the attenuation coefficient is estimated by introducing an underwater optical attenuation model into the RANSAC frame [23]. Meanwhile, a depth map is estimated from the combination of motion structure technology and model-based restoration. The simulation and real image test results showed that the method restores the image, thus improving the ability of target recognition and feature matching.
Cheng et al. [24] proposed a method for image fusion based on the Mueller matrix to enhance the quality of underwater degradation images. Each Mueller matrix element image is given a weight and fused to generate a new image. The optimal weights are obtained by searching for values that maximize the image quality. The validity of this method was proved by comparison with the Mueller matrix image and the latest method using objective and subjective analysis. Moreover, the image was enhanced using analog weights. Due to the nature of the Mueller matrix, this method improves the underwater observation distance and image quality, and provides the enhanced images with information that is unavailable when conventional methods are used.
Due to the absorption and scattering of light in water, color projection and poor contrast are often present in underwater images. Zhou et al. [25] proposed an underwater image restoration method based on a priori underwater features. They first established a powerful model to estimate the background light based on the characteristics of flatness, hue, and brightness, thus effectively mitigating color distortion. The red channel of the color-corrected image was then compensated to correct its transmission map. The rough transmission diagram was refined by combining it with a structure-guided filter.

3. Underwater Image Processing Based on Polarization Imaging and Scattering

The most difficult problem encountered during optical detection in turbid areas is the scattering effect of water on the light wave, which mainly results in low image contrast, a reduction in resolution, and image blurring. Scattering includes forward and backward scattering processes. When forward scattering occurs, light deviates from the original transmission path, resulting in a reduced image resolution and blurred image. Backscattered light, which carries suspended particulate information, produces a ‘curtain effect’ on the target image and reduces the image contrast. Therefore, it is necessary to overcome the scattering and reflection problems in underwater imaging to improve the imaging distance and quality.
Polarized imaging technology has obvious advantages in removing background scattered light and achieving clear underwater images by deeply mining the uniqueness and differences in polarization information in a scattered light field. Currently, accurate estimation of the polarization characteristics and relationship between target information light and background scattered light, inverting the intensity distribution of target information light and background scattered light, are key research areas of underwater imaging technology. Research has shown that the polarization characteristics of incident polarized light can be used to separate these two kinds of light in a scene, effectively restoring a clear scene, improving the contrast and clarity of imaging results, and aiding underwater target detection and recognition. Because underwater search and rescue operations often face target detection problems in high-turbidity water, exploration of polarization imaging technology that is suitable for turbid water is necessary.
Underwater models for studying turbidity and illumination have been established, which is helpful for optical research of target detection and recognition in turbid waters. Bailey et al. [26] proposed a model based on the spatial variation in underwater environments and coherent light and used it for low-contrast target detection in turbid water. This model was used to theoretically study the effects of turbidity, projection space-frequency variation, and three-dimensional target shapes on unstructured scattered light components and the target structured return signal. The results showed that the model’s accuracy is adequate for the modeling of noise reduction technology. This result indicates that the received three-dimensional target image can be modeled with backscattering and structured illumination, and noise reduction and target identification can be achieved in the model environment. Based on the image degradation model, Han [27] considered image degradation due to the joint effects of forward and backward scattered light, estimated the degradation function of forward scattered light using the edge method, and further restored clear scene images. He constructed a turbid water polarization imaging model, and then obtained the polarization degree of target information light and background scattered light using the optical correlation principle to restore the clear scene.
On the premise of establishing an optical model, Han [27] proposed an active underwater polarization imaging method, which is based on the imaging noise analysis model, and studied the effect of noise that is introduced during the polarization imaging process on the final imaging quality. This method resulted in the best polarization azimuth image for active underwater polarization imaging, and the relationship between different polarizer images and the final imaging quality was established. This method can effectively realize the imaging distance in a high-turbidity water body, improve the imaging quality and detection effect, and providing support for underwater search and rescue work in rivers and offshore areas.
Huang [28] proposed a polarization image restoration algorithm and a new curve fitting-based method to estimate the target signal of polarization difference images. Based on the polarization effect of reflected light in underwater imaging, the former was used to restore underwater blurred images with polarization imaging, and study the imaging model of underwater active illumination imaging systems and the transmission behavior of polarization information in an underwater turbid medium. The latter considers the polarization effect of the reflected light from the object in the scene to derive the true transmission coefficient image and underwater restored image. Both can overcome the invalid detection problem in the area corresponding to objects with a low degree of deflection and effectively enhance the underwater imaging quality.
As backscattered light occurs due to the presence of high concentrations of impurities in turbid water, the reflected light from an object is easily confused, which makes it difficult to distinguish the object from the environment. Therefore, the division of reflected light from interfering light, such as backscattered light, which reflects the characteristic of the object, is a core issue for underwater image processing using polarized imaging. At present, several methods, such as optical sensing technology, the polarization filter method, and the backscattering interference suppression method, that can separate coherent light from incoherent light exist. Cochenour et al. [29] proposed new optical sensing technology based on the orbital angular momentum (OAM). The target is illuminated by a Gauss beam. By setting a diffraction spiral phase plate at the receiving end, the reflected and backscattered light of the object passes through the phase plate to form vortex light, thus spatial separation of coherent and incoherent light is achieved. Experiments have shown that the echo of a ballistic target can achieve detection that is two to three orders of magnitude the level of backscattered clutter. The detection of this coherent element is realized using a complex optical heterodyne scheme. In addition, the detection of this small coherent signal is completed without the use of any complex optical heterodyne scheme, which indicates that the unique characteristics of OAM can be used to distinguish between objects and the environment. Amer et al. [30] used a polarized imaging optical system to reduce the influence of underwater beam diffusion on image acquisition and optimized the DCP method. They used a low-pass polarization Gauss filter to calculate the illumination from the input image and enhance underwater optical imaging, which reduced the long running time and ameliorated the efficiency reduction of traditional algorithms with the increase in turbidity. Moreover, the visibility of this method was significantly higher than that of the traditional DCP method and the processing time was reduced by nearly 50-fold. Zhao et al. [31] proposed an underwater image restoration method based on transmission correction for when the object polarization effect cannot be ignored. Without sacrificing the quality of image restoration, this method showed a better performance, has a simpler algorithm, and used less computational time than previous methods. This approach converts the transmittance of a low depolarized object from a negative value to a positive value and uses a simple polynomial fitting algorithm to optimize the image quality. The results showed that it can effectively improve the quality of underwater images regardless of the degree of depolarization of the target. The suppression of background backscattering interference also combines polarization imaging technology with image processing technology, which has greater engineering application value. Zhao [32] proposed a target detection algorithm based on guided filtering combined with ViBe and a secondary target detection algorithm based on polarization difference image and intensity image fusion combining image processing and polarization imaging technology. To eliminate the influence of the backscattering of scattered particles in turbid media, enrich the detailed information of the detection target, and improve the effectiveness and practicability of the target detection algorithm, the researchers established a database containing the light polarization characteristics of different turbid media and typical targets to provide prior knowledge for information analysis and target detection of polarization imaging in certain circumstances.
Regarding the significant light scattering effect evident in mixed water, a new method for target detection by backscattering was proposed. Wu et al. [33] proposed a method for target detection and geometric contour analysis based on backscattering asymmetry. The asymmetry of the returned beam was observed by using different propagation depths, transverse coherence lengths, and propagation angles of the beam. This asymmetric model was converted into a surface inclination angle and different surface gradients were obtained by collecting two-beam scans to reconstruct the basic surface contours of the target. The experimental results showed that the asymmetric method is capable of target detection and contour analysis using the differential parts of the backscattered signal when the backscattered light interference is strong. This method is also compatible with continuous-wave and pulsed lasers, which can provide a low-cost platform using a continuous wave laser or can be combined with the time-of-flight concept, using a pulsed laser to enhance the effect.

4. Other Methods

Other methods for target detection and recognition in turbid waters also exist, which are not restricted to inertial thinking. It is necessary to explore other methods. For example, for subsea pipelines and underwater pipelines, sonar can effectively identify targets, and electronic communications can also be used for target detection and positioning.
Liu [34] found that forward-looking sonar is prone to interference when imaging linear targets in water. The geometric, grayscale, and statistical features of such interferences are similar to real targets, which easily leads to misjudgment and omission of the visual system. Therefore, underwater linear target detection technology combining the Hough transform and threshold segmentation has been proposed. This method combines the Hough transform and threshold segmentation and uses the Hough transform to restrict the orientation of threshold segmentation. Meanwhile, threshold segmentation provides a detection basis for the Hough transform, and the two interact. When the statistical characteristics of the change in the segmentation area with the attitude are used as the objective evaluation conditions, this method can effectively extract linear objects, such as pipes, and the straight-line part of the target area is neat, with less external noise. For sonar images from other angles that are not displayed, the segmentation effect of this method is still better than some other segmentation methods.
In nature, weak-current fish generate electric fields, which are used for navigation and target detection, and the recognition of terrain and prey. Inspired by this biology, Chen et al. [35] studied the effect of obstacles on electronic communication in quasi-two-dimensional water environments with bionic electronic communication systems. They first applied the Fresnel zone theory to theoretically analyze the influence of obstacles on electronic communication, simplified the marine terrain obstacles, and used ANSYS Maxwell to simulate the impact of these obstacles on electronic communication. The simulation and experiment results showed that the material, relative position, geometry, and size of the obstacles have different effects on electronic communication.

5. Engineering Technology Summary

In terms of the division of technical application fields, underwater operation in various turbid areas requires different technical support. The deep-sea engineering detection area is a typical ‘deep and clear’ water area, for which the main difficulty is the shortage of light, which leads to low-contrast and blurred images. Therefore, the construction of a model for the optical environment of the seafloor in necessary or an artificial light source or polarization imaging should be used for image processing. ‘Shallow and turbid’ waters often have plenty of light and do not required an artificial light source, but the turbidity of the water is high, and the background scattered light intensity is strong, so an image restoration model should be established, and target features extracted by removing background scattered light interference. Deep-water aquaculture farms represent a typical ‘deep and turbid’ area, with a turbid water body, poor light transmittance, and high aquatic biological density. Moreover, compared with deep-sea engineering work areas, it is more likely that the engineering requirements include identification of the quantity, type, health status, and movement tracking of aquatic organisms. These projects vary according to the actual conditions. Therefore, deep learning and ML should be flexibly used to achieve engineering automation. This combines underwater image restoration and enhancement techniques to improve the accuracy of depth learning methods.

6. Datasets for Target Detection and Recognition in Turbid Water

Due to the uniqueness of turbid waters, underwater image datasets are scarce, and have high acquisition costs. Rich and open-source datasets support deep learning theory and effective application, so greater attention should be given to the collection of underwater image datasets in the future. The Open Image Dataset [36], as a composite dataset, contains about 9 million images spanning about 6000 categories and contains more real-life entities than ImageNet. It contains a significant number of aquatic images and underwater image data, which provide sufficient training data for training depth learning network models.
The Brackish dataset [37] is a real underwater biological video dataset, captured 9 m from the water surface at the bottom of Limfjorden, Denmark. This dataset divides videos by frame into more than 14,000 images with a size of 960 × 540. It contains more than 11,000 images, including targets to be detected, and more than 3000 images with undetected targets. The entire dataset consists of six categories: big fish, small fish, crab, shrimp, and jellyfish.
The uniqueness of the dataset is that it contains many small aquatic organisms, especially small fish and crabs. The distribution of small fish is centralized, and the dataset also contains some incomplete images of detected targets.
The Underwater Image Enhancement Benchmark (UIEB) [38] includes 950 real underwater images, of which 890 have corresponding reference images, and the other 60 underwater images that do not have better reference images are used as challenging data. Using this dataset, the most advanced underwater image enhancement algorithms can be comprehensively studied, which is suitable for training CNN.
The Stereo Quantitative Underwater Image Dataset (SQUID) [39] is an image dataset that contains images taken at various locations, with different water properties, and displays color charts in the scene. This dataset can quantitatively evaluate the restoration algorithm of natural images.


  1. Girshick, R.; Donahue, J.; Darrell, T.; Malik, J. Rich feature hierarchies for accurate object detection and semantic segmentation. In Proceedings of the IEEE 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, OH, USA, 23–28 June 2014.
  2. Girshick, R. Fast R-CNN. In Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile, 7–13 December 2015.
  3. Ren, S.Q.; He, K.M.; Girshick, R.; Sun, J. Faster R-CNN: Towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 2017, 39, 1137–1149.
  4. Liu, W.; Anguelov, D.; Erhan, D.; Szegedy, C.; Reed, S.; Fu, C.Y.; Berg, A.C. SSD: Single Shot multi-box Detector. In Computer Vision—ECCV 2016; Leibe, B., Matas, J., Sebe, N., Welling, M., Eds.; Springer: Cham, Switzerland, 2016; Volume 9905, pp. 21–37.
  5. Redmon, J.; Divvala, S.; Girshick, R.; Farhadi, A. You only look once: Unified, real-time object detection. In Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 26 June–1 July 2016; pp. 779–788.
  6. Redmon, J.; Farhadi, A. Yolo9000: Better, faster, stronger. In Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, 21–26 July 2017; pp. 6517–6525.
  7. Redmon, J.; Farhadi, A. Yolov3: An incremental improvement. arXiv 2018, arXiv:1804.02767.
  8. Lakshmi, M.D.; Santhanam, S.M. Underwater Image Recognition Detector using Deep ConvNet. In Proceedings of the 2020 National Conference on Communications (NCC), Kharagpur, India, 21–23 February 2020.
  9. Liu, Y.; Meng, W.; Zong, H. Jellyfish Recognition and Density Calculation Based on Image Processing and Deep Learning. In Proceedings of the 2020 Chinese Control and Decision Conference (CCDC), Hefei, China, 22–24 August 2020.
  10. Palconit, M.G.B.; Almero, V.J.D.; Rosales, M.A.; Sybingco, E.; Bandala, A.A.; Vicerra, R.R.P.; Dadios, E.P. Towards Tracking: Investigation of Genetic Algorithm and LSTM as Fish Trajectory Predictors in Turbid Water. In Proceedings of the 2020 IEEE Region 10 Conference (TENCON), Osaka, Japan, 16–19 November 2020.
  11. Li, H.; Ji, S.; Tan, X.; Li, Z.; Xiang, Y.; Lv, P.; Duan, H. Effect of Reynolds number on drag reduction in turbulent boundary layer flow over liquid–gas interface. Phys. Fluids 2020, 32, 122111.
  12. Thomas, R.; Thampi, L.; Kamal, S.; Balakrishnan, A.A.; Mithun Haridas, T.P.; Supriya, M.H. Dehazing Underwater Images Using Encoder Decoder Based Generic Model-Agnostic Convolutional Neural Network. In Proceedings of the 2021 International Symposium on Ocean Technology (SYMPOL), Kochi, India, 9–11 December 2021; pp. 1–4.
  13. Wang, Z.; Bovik, A.C.; Sheikh, H.R.; Simoncelli, E.P. Image quality assessment: From error measurement to structural similarity. IEEE Trans. Image Process 2004, 13, 600–612.
  14. Dudhane, A.; Patil, P.W.; Murala, S. An End-to-End Network for Image De-Hazing and Beyond. IEEE Trans. Emerg. Top. Comput. Intell. 2020, 6, 159–170.
  15. Yin, X.; Ma, J. General Model-Agnostic Transfer Learning for Natural Degradation Image Enhancement. In Proceedings of the 2021 International Symposium on Computer Technology and Information Science (ISCTIS), Guilin, China, 4–6 June 2021; pp. 250–257.
  16. Li, C.; Anwar, S.; Porikli, F. Underwater scene prior inspired deep underwater image and video enhancement. Pattern Recognit. 2020, 98, 107038.
  17. Islam, M.J.; Xia, Y.; Sattar, J. Fast Underwater Image Enhancement for Improved Visual Perception. IEEE Robot. Autom. Lett. 2020, 5, 3227–3234.
  18. Cecilia, S.M.; Murugan, S.S. Edge Aware Turbidity Restoration of Single Shallow Coastal Water Image. J. Phys. Conf. Ser. 2021, 1911, 27–29.
  19. Zhou, J.C.; Yang, T.Y.; Ren, W.Q.; Zhang, D.; Zhang, W.S. Underwater image restoration via depth map and illumination estimation based on single image. Opt. Express 2021, 29, 29864–29886.
  20. Li, Y.; Lu, H.; Li, J.; Li, X.; Li, Y.; Serikawa, S. Underwater image de-scattering and classification by deep neural network. Comput. Electr. Eng. 2016, 54, 68–77.
  21. Yang, L.M. Research on the Target Enhancement Technology Based on Polarization Imaging. Master’s Thesis, Xi’an University of Technology, Xian, China, 28 May 2018.
  22. Drews, P., Jr.; Nascimento, E.R.; Campos, M.; Elfes, A. Automatic restoration of underwater monocular sequences of images. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots & Systems, Hamburg, Germany, 28 September–3 October 2015.
  23. Mach, C. Random Sample Consensus: A paradigm for model fitting with application to image analysis and automated cartography. Commun. ACM 1981, 24, 381–395.
  24. Cheng, H.; Wan, Z.; Zhang, R.; Chu, J. Visibility improvement in turbid water by the fusion technology of Mueller matrix images. In Proceedings of the 2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA), Dalian, China, 28–30 June 2021; pp. 838–842.
  25. Zhou, J.C.; Wang, Y.Y.; Zhang, W.S.; Li, C.Y. Underwater image restoration via feature priors to estimate background light and optimized transmission map. Opt. Express 2021, 29, 28228–28245.
  26. Bailey, B.C.; Blatt, J.H.; Caimi, F.M. Radiative Transfer Modeling and Analysis of Spatially Variant and Coherent Illumination for Undersea Object Detection. IEEE J. Ocean. Eng. 2003, 28, 570–582.
  27. Han, P.L. Research on Polarization Imaging Exploration Technology of Underwater Target. Ph.D. Thesis, Xi’an University of Electronic Science and Technology, Xian, China, June 2018.
  28. Huang, B.J. Optimization Technology of Polarization Imaging Contrast in Complex Environment. Master’s Thesis, Tianjin University, Tianjin, China, December 2016.
  29. Cochenour, B.; Rodgers, L.; Laux, A.; Mullen, L.; Morgan, K.; Miller, J.K.; Johnson, E.G. The detection of objects in a turbid underwater medium using orbital angular momentum (OAM). In Proceedings of the SPIE Defense + Security 2017, Anaheim, CA, USA, 9–13 April 2017. Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series.
  30. Amer, K.O.; Elbouz, M.; Alfalou, A.; Brosseau, C.; Hajjami, J. Enhancing underwater optical imaging by using a low-pass polarization filter. Opt. Express 2019, 27, 621.
  31. Hui, W.; Zhao, L.; Huang, B.; Li, X.; Wang, H.; Liu, T. Enhancing Visibility of Polarimetric Underwater Image by Transmittance Correction. IEEE Photonics J. 2017, 9, 6802310.
  32. Zhao, R.X. Research on the Target Detection Method in Turbid Media Based on Polarization Differential Imaging. Master’s Thesis, Nanjing University of Technology, Nanjing, China, 2020.
  33. Wu, C.; Lee, R.; Davis, C. Object detection and geometric profiling through dirty water media using asymmetry properties of backscattered signals. In Proceedings of the Ocean Sensing and Monitoring X, Orlando, FL, USA, 15–18 April 2018; Volume 10631.
  34. Liu, L.X. Research on Target Detection and Tracking Technology of Imaging Sonar. Ph.D. Thesis, Harbin Engineering University, Harbin, China, 20 November 2015.
  35. Chen, Y.T.; Wang, W.; Li, L.; Kelly, R.; Xie, G. Obstacle effects on electrocommunication with applications to object detection of underwater robots. Bioinspiration Biomim. 2019, 14, 056011.
  36. Github. Available online: (accessed on 30 January 2022).
  37. Pedersen, M.; Haurum, J.B.; Gade, R.; Moeslund, T.B. Detection of Marine Animals in a New Underwater Dataset with Varying Visibility. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, Long Beach, CA, USA, 15–20 June 2019.
  38. Li, C.; Guo, C.; Ren, W.; Cong, R.; Hou, J.; Kwong, S.; Tao, D. An Underwater Image Enhancement Benchmark Dataset and Beyond. IEEE Trans. Image Process. 2019, 29, 4376–4389.
  39. Cecilia, S.M.; Murugan, S.; Padmapriya, N. Analysis of Various Dehazing Algorithms for Underwater Images. In Proceedings of the 2019 International Symposium on Ocean Technology (SYMPOL), SSN College of Engineering, Chennai, India, 11–13 December 2019.
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