Concrete-Crack Detection on Railway Sleepers: Comparison
Please note this is a comparison between Version 2 by Sirius Huang and Version 1 by Md. Al-Masrur Khan.

Crack inspection in railway sleepers is crucial for ensuring rail safety and avoiding deadly accidents. Traditional methods for detecting cracks on railway sleepers are very time-consuming and lack efficiency. Therefore, researchers are paying attention to vision-based algorithms, especially Deep Learning algorithms.

  • crack detection
  • crack quantification
  • railway sleeper

1. Introduction

In South Korea traveling by train is one of the most convenient modes of transportation. A study showed that the railroads in South Korea cover a length of 3688 Km. A railroad commonly consists of steel rail, ballast bed, railway sleeper, railway fastener, and other parts of a railway track. Among all the components, the sleeper is a crucial component of a railway track. The principal roles of a railway sleeper include distributing loads of the trains from the steel rails to the ballast bed, reducing track movement, and stabilizing the track gauge during train travel, all of which assure safe travel for the train passengers. Timber sleepers, concrete sleepers, and to a lesser extent, steel sleepers reinforce polymeric sleepers that are commonly used on railway tracks. However, literature shows that concrete sleepers are mostly used (60–80%) for several advantages in a railway network [1]. These concrete sleepers can be damaged in the form of cracks due to rain erosion, exposure to the sun, the reaction of salts in the earth with concrete sleepers, long-term navigation, an overload of trains, and so on. These cracks can introduce dangerous situations and can cause deadly accidents based on the daily loads of traffic and the severity of the cracks. So, detecting cracks early is crucial for inspecting and assessing the usability of concrete sleepers. Over the years, manual inspection has been a common and traditional method to detect cracks in railway sleepers. However, physical assessment has blind spots, and it lacks accuracy. Furthermore, this approach is time-consuming, labor-intensive, and costly. Inspectors rely solely on their human vision while traversing railway tracks to detect cracks. Besides these, the inspection performance can be varied from time to time based on the experience level of the inspector. Replacing the manual inspection with vision-based technologies can overcome these problems, and the sleeper cracks can be detected effectively. Recently, many advances have been made in the computer vision field. And, the image-based techniques have already shown mesmerizing performance in other concrete structures like concrete pavement [2], buildings [3], tunnels [4], concrete bridges [5], etc. The image processing techniques make the concrete crack detection task fast and accurate. Therefore, utilizing the image processing methods will ensure automated sleeper crack detection and alleviate the personnel’s tedious and repetitive tasks. There are primarily two types of processing methods. The first method is to extract the features, remove the noises, and then implement classification. The image features are extracted by different types of feature extraction methods like wavelet transformation [6], Percolation methods [7], Otsu’s method [8], the morphological approach [9], etc. After extracting the features, classification algorithms are employed to classify the cracks in the images. Traditional image processing methods need to select the most relevant feature extraction approach. Even though numerous image feature extraction methods exist, there is no global feature extraction strategy to cope with images in various situations. As a result, a significant series of experiments or even novel feature extraction feature techniques must be devised to identify a suitable feature extraction approach for a specific situation. However, these methods fail to deal with images having complex patterns, extreme noises, and intensity inhomogeneity.
The other approach to image processing is utilizing the Deep Learning (DL) technology, especially the Convolutional Neural Network (CNN). CNN utilizes many hidden Neural Network (NN) layers to extract the underlying features automatically and classify the images. With the development of the AlexNet [10] using CNN in 2012, the advancement curve observed a huge surge in the field of computer vision. Following this, other CNN models with various depths have been designed. The classification was further increased in 2014 by using the newly developed VGG [11], and GoogleNet [12] model. Understanding the advancement of the CNN model and considering the necessity of monitoring the cracks in concrete structures, including railway sleepers, researchers nowadays are more inclined to utilize CNN models for detecting cracks. Zhang et al. proposed a Convolutional Neural Network (CNN) classifier in 2016 for detecting cracks in concrete structures [13]. The main aim of this research was to design a classifier based on patches for detecting cracks in concrete structures. After that, many other CNN models have been designed for solving crack image classification, detection, and segmentation tasks. Among the three types of solutions, crack segmentation has become the most popular research. Crack segmentation provides pixel-wise classification, where each of the pixels are classified as cracks or non-cracks. The predicted image from the CNN-based segmentation model highlights the cracks on the image and provides an idea about the cracks’ location and geometric shape. Furthermore, the segmented pictures can be utilized to extract a few key pieces of information (such as crack length, width, and area) that can estimate the severity of cracks in concrete sleepers. Though there are already many developed DL models, and though past research on utilizing CNNS to detect concrete cracks has generated significant results, only a few research studies have been conducted for segmenting cracks on railway sleepers using the CNNS. Despite a little research, there is still room to develop new methods to achieve higher accuracy and contribute to railway sleeper crack detection.

2. Vision Based Crack Detection Methods

Many vision-based crack identification methods have already been suggested for solving the problem of manual inspection in the case of detecting cracks in concrete structures. One type of Computer Vision (CV)-based technique is the use of some traditional approaches. For example, Qu et al. utilized a percolation-based image processing approach to identify cracks in concrete. The authors first extracted the cracks using the genetic programming method. Later, after calculating the crack tip, they used the high-precision percolation method to detect small cracks [7]. Hoang et al. proposed a Min–Max Gray Level Discrimination (M2GLD) method to integrate with the Otsu method for detecting cracks in building structures. Their model also could find out a few crack characteristics, e.g., width, area, parameter [14]. Fujita et al. considered crack detection on noisy concrete structures. The authors used the subtraction pre-processing method for removing noises like shades and bad illumination conditions and introduced the Hessian matrix for differentiating the concrete cracks from the background [15]. Hutchison et al. presented a hybrid method based on the FHT algorithm and Canny edge detector for detecting cracks in concrete structures. The authors also calculated the parameters of the predicted cracks [16]. However, the primary limitation of these classical IPTs is that the techniques pay more attention to extracting local features rather than global properties like cracks on an image, which may downgrade the detection task. So, to improve the crack detection task, researchers started combining classification methods with the classical IPTs. As a consequence, Jahanshahi et al. developed a unique approach based on morphological operations and classifier techniques. The morphological operator was used to extract the necessary features, and the classifier algorithms classified real cracks [17]. Shi et al. presented a new framework named Crack-Forest based on Random Structured Forest for detecting cracks as well as characterizing the cracks on concrete roads [18]. Chun et al. presented a hybrid crack detection framework by combining a canny edge detector for extracting geometric features of the cracks and a supervised ML algorithm named Light Gradient Boosting Machine (LightGBM). The authors evaluated their framework by using photos containing cracks with adverse conditions [19]. In recent years, Deep Learning (DL) models have outperformed classical CV-based techniques for detecting objects. Furthermore, DL models do not need external feature learning; they can perceive features from a significant quantity of input data. Therefore, researchers are paying attention to detecting concrete cracks with more precision using DL algorithms. Cha et al. introduced a Convolutional Neural Network (CNN) classifier and a sliding window technique for detecting cracks in concrete structures. The authors tested the robustness of their model by predicting external images and showed that their model outperformed the Sobel and Canny edge detection method [20]. Xu et al. presented a DL model by using a Restricted Boltzmann Machine (RBM) algorithm to detect cracks on bridge structures. The authors trained their model with consumer-grade camera images and used a divergence learning algorithm to obtain optimal parameters [21]. Chen et al. introduced a novel DL framework named NB-CNN by fusing Naive Bayes data with a CNN classifier for extracting cracks on a nuclear power plant from video frames. Their method could maintain the spatiotemporal video coherence and produce better results than LBP-SVM [22]. Maeda et al. presented a benchmark road crack dataset for the first time using smartphone images and used a CNN to classify eight types of cracks on road surfaces [23]. Zhang et al. focused on both improving crack detection accuracy and reducing the training time of the Deep Learning model. As a consequence, they developed a Deep Learning model named MobileNetV3-BLS based on MobileNetV3 to detect cracks on concrete surfaces. The authors also claimed that the weights of their method can be updated quickly, which helps to obtain increased accuracy with newly added nodes [24]. Nguyen et al. proposed a Deep Learning classifier to detect concrete cracks. The authors utilized a genetic algorithm to optimize the parameters of the image processing technique [25]. Katsigiannis et al. created a dataset of brickwork masonry facades and built a Deep Learning model using the transfer learning strategy to detect cracks on the masonry facades using their limited data [26]. Deng et al. proposed the You Only Look Once (YOLO) version 2 model for locating cracks with bounding boxes on concrete structures. The model was able to distinguish cracks from handwritten scripts present in the concrete structure [27]. Hyuan et al. presented a method called Crack Deep Network (CrackDN) based on Faster Region CNN (Fast RCNN) to identify sealed and unsealed cracks having diverse backgrounds in pavement images. The authors extracted features by a Zeiler-Fragus Network (ZF-Net)-based CNN embedded with a sensitivity network in parallel. Finally, they utilized a Region proposal Refinement Network (RPRN) for classifying the cracks [28]. Jian et al. proposed a modified YOLO-V5 network by adding a swin transformer and a bidirectional feature pyramid. The authors claimed that they obtained better performance than the YOLO-V7 model [29]. Chen et al. considered the problem of variable illumination conditions in the case of crack detection in their work [30]. To solve the issue, the authors proposed a model named IlumiCrack, which uses a Gaussian model to play with the brightness of pictures and an SGD model to detect the cracks. However, these models are capable of categorizing and localizing cracks within a concrete structure, but they cannot detect cracks at the individual pixel level. Therefore, among the DL models nowadays, encoder–decoder-based pixel-level crack detection models (i.e., FCN [31], U-net [32]) are becoming more popular for improving the detection accuracy as these models can extract the geometrical shape of the cracks along with localizing them. Li et al. proposed a novel encoder–decoder-based model called an FCN for detecting cracks where the VGG19 model was used as the downsampler of the proposed FCN. After predicting the crack images, the authors also generated crack skeletons to measure morphological features [33]. Bang et al. proposed an FCN model based on the ResNet-152 encoder network for detecting pavement cracks from black-box camera images. The authors examined their model with transfer learning and without the transfer learning processes. However, Resnet-152 with transfer learning performed better [34]. Manjurul et al. proposed an FCN model using the VGG16 as an encoder network to detect cracks on concrete surfaces. The authors tested their model on a benchmark dataset and showed that their model obtained a 10.93% and 20.93% improvement over the CNN and SVM model, respectively, with respect to the SA [35]. Liu et al. adopted U-net, which is another encoder–decoder-based network (the improved version of FCN), to segment cracks in concrete structures. The authors introduced the focal loss function for handling the class imbalance problem in their work [36]. Ji et al. also utilized the U-net model with zero paddings in their work for automatically detecting cracks in concrete structures. They trained the model using 200 images collected by an unmanned aerial vehicle and obtained better results than the Canny and Sobel method [37]. However, U-net models can also fall behind in predicting extremely narrow cracks and detecting cracks in adverse conditions. So, nowadays, researchers are continuously integrating different approaches with U-net to address these challenges. Yan et al. proposed a model called Res-Unet by incorporating residual connections to the original U-net for detecting cracks in the concrete bridge structures [38]. Chen et al. integrated a switch module named SWM with the U-net architecture to boost the running speed and reduce the computational complexity of the U-net. The SWM model allows the pixel classification result obtained by the VGG13 encoder to be passed into the decode module if there is a crack; otherwise, it just discards the result to save the computation time [39]. Sun et al. considered the problem of detecting thin cracks with adverse environmental conditions in concrete structures. For this, the authors modified the U-net architecture by adding a Pyramid Pooling Module (PPM) into it, and the model successfully predicted the thin cracks [40]. Lin et al. proposed a U-net model with an attention mechanism to detect cracks on concrete structures. The authors utilized the attention gate module in three different fashions (i.e., Attention U-net, Advanced Attention U-net, and Full Attention U-net). They remarked that the full attention strategy was the best for detecting cracks with less computation complexity [41]. Augustauskas et al. improved the U-net model by adding residual blocks, an atrous spatial pyramid pooling module, and an attention gate module for detecting concrete cracks. The authors tested their model with a few datasets and showed by an ablation study that the improved model outperformed U-net with no notable computational complexity [42]. With the period, researchers have developed other encoder–decoder-based architectures as well, rather than improving the U-net. For example, Li et al. proposed a model named HrSegNet to improve the inference speed of crack segmentation while preserving the crack details [43]. Wang et al. tackled the challenge of high computation complexity during the training of a crack segmentation network in their work [44]. The authors developed their model based on a student–teacher framework. They utilized channel-wise distillation knowledge to make the model lightweight. Yang et al. developed a model named PAF-Net by incorporating a feature fusion technique to mitigate semantic gap issues during crack segmentation in concrete structures [45]. Khan et al. proposed a segmentation model named RCDNet to detect cracks on pavement structures in real-time [46]. The authors incorporated attention modules to increase the accuracy of the model without any computational head.

3. Crack Detection on Railway Sleepers

Though there are many research works for detecting cracks on various concrete structures, there are not many equivalent works to detect cracks on railway sleepers. Rather, vision-based techniques are employed to a lower extent for solving some other similar types of problems in railway industries. For example, Saha et al. detected cracks on railway tracks using a vision-based technique by including edge detection methodology [47]. Fan et al. presented a method by combining local binary features and an SVM classifier for detecting defective fasteners in railway tracks [48]. Mehmet et al. employed a method based on a canny edge extractor and hough transformation to monitor the condition of the railway components [49]. Crack monitoring in rail tracks can be viewed from various perspectives, each with its own set of objectives. Thendral et al. presented a machine vision system for detecting cracks on railway tracks. The authors first extracted the features using the Gabor transform and passed those features to a neural network classifier. They achieved an overall accuracy of 94.9% during detecting the cracks [50]. Min et al. detected cracks on railway tracks using two steps. First, the authors found the tracks using the features of the hue channel, and later, they performed contour-based surface profiling to classify the defects [51]. Sajjad et al. detected cracks not only on wooden sleepers, but also on concrete sleepers by utilizing a vision-based technique. However, as the authors developed the system based on a binary thresholding technique, it may lose robustness during environmental and illumination changes [52]. Delfourazi et al. proposed a crack detection method for railway sleepers using the template matching technique first to detect the concrete sleepers. Then, they used linear SVM and Radial-basis Function SVM (RBF-SVM) to classify the crack types on the sleepers. Numerical results showed that RBF-SV performed better [53]. Kim et al. proposed an advanced method using the Adaboost algorithm for detecting cracks on railway sleepers. Their algorithm identified the cracks with an identification rate of more than 90% [54]. Wang et al. proposed a two-stage algorithm for detecting cracks on concrete railway sleepers with less computation time. First, they used an edge detection technique called neighborhood range algorithm for selecting crack areas and then utilized CNN on top of it to successfully classify the crack types [55]. Xia et al. presented a novel framework named CF-NET based on the RetinaNet object detection framework to detect cracks with bounding boxes on railway sleepers [56]. Jang et al. proposed a modified version of Single Shot Detector for detecting cracks on railway sleepers. The authors compared between two images and detected deformed regions to find the cracks [57].


  1. International Union of Railways—The Worldwide Railway Organisation. UIC. Available online: (accessed on 14 December 2023).
  2. Tang, Y.; Zhang, A.A.; Luo, L.; Wang, G.; Yang, E. Pixel-level pavement crack segmentation with encoder-decoder network. Measurement 2021, 184, 109914.
  3. Zheng, M.; Lei, Z.; Zhang, K. Intelligent detection of building cracks based on Deep Learning. Image Vis. Comput. 2020, 103, 103987.
  4. Ren, Y.; Huang, J.; Hong, Z.; Lu, W.; Yin, J.; Zou, L.; Shen, X. Image-based concrete crack detection in tunnels using deep fully convolutional networks. Constr. Build. Mater. 2020, 234, 117367.
  5. Fu, H.; Meng, D.; Li, W.; Wang, Y. Bridge Crack Semantic segmentation based on improved deeplabv3+. J. Mar. Sci. Eng. 2021, 9, 671.
  6. Nigam, R.; Singh, S.K. Crack detection in a beam using wavelet transform and photographic measurements. Structures 2020, 25, 436–447.
  7. Qu, Z.; Chen, Y.-X.; Liu, L.; Xie, Y.; Zhou, Q. The Algorithm of Concrete Surface Crack Detection Based on the Genetic Programming and Percolation Model. IEEE Access 2019, 7, 57592–57603.
  8. Chen, B.; Zhang, X.; Wang, R.; Li, Z.; Deng, W. Detect concrete cracks based on Otsu algorithm with Differential Image. J. Eng. 2019, 2019, 9088–9091.
  9. Hou, H.; Lin, W. A new approach for the detection of concrete cracks based on adaptive morphological filtering. In Fuzzy Systems and Data Mining VI; IOS Press: Amsterdam, The Netherlands, 2020.
  10. Krizhevsky, A.; Sutskever, I.; Hinton, G.E. ImageNet classification with deep convolutional Neural Networks. Commun. ACM 2017, 60, 84–90.
  11. Simonyan, K.; Zisserman, A. Very deep convolutional networks for large-scale image recognition. arXiv 2014, arXiv:1409.1556.
  12. Szegedy, C.; Liu, W.; Jia, Y.; Sermanet, P.; Reed, S.; Anguelov, D.; Erhan, D.; Vanhoucke, V.; Rabinovich, A. Going deeper with convolutions. In Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA, 7–12 June September 2014; pp. 1–9.
  13. Zhang, L.; Yang, F.; Zhang, Y.D.; Zhu, Y.J. Road crack detection using deep convolutional neural network. In Proceedings of the 2016 IEEE International Conference on Image Processing (ICIP), Phoenix, AZ, USA, 25–28 September 2016; pp. 3708–3712.
  14. Hoang, N.-D. Detection of surface crack in building structures using image processing technique with an improved Otsu method for image thresholding. Adv. Civ. Eng. 2018, 2018, 3924120.
  15. Fujita, Y.; Hamamoto, Y. A robust automatic crack detection method from noisy concrete surfaces. Mach. Vis. Appl. 2010, 22, 245–254.
  16. Hutchinson, T.C.; Chen, Z.Q. Improved image analysis for evaluating concrete damage. J. Comput. Civ. Eng. 2006, 20, 210–216.
  17. Jahanshahi, M.R.; Masri, S.F.; Padgett, C.W.; Sukhatme, G.S. An innovative methodology for detection and quantification of cracks through incorporation of depth perception. Mach. Vis. Appl. 2011, 24, 227–241.
  18. Shi, Y.; Cui, L.; Qi, Z.; Meng, F.; Chen, Z. Automatic Road Crack Detection Using Random Structured Forests. IEEE Trans. Intell. Transp. Syst. 2016, 17, 3434–3445.
  19. Chun, P.; Izumi, S.; Yamane, T. Automatic detection method of cracks from concrete surface imagery using two-step light gradient boosting machine. Comput.-Aided Civ. Infrastruct. Eng. 2020, 36, 61–72.
  20. Cha, Y.-J.; Choi, W.; Büyüköztürk, O. Deep learning-based crack damage detection using convolutional neural networks. Comput.-Aided Civ. Infrastruct. Eng. 2017, 32, 361–378.
  21. Xu, Y.; Li, S.; Zhang, D.; Jin, Y.; Zhang, F.; Li, N.; Li, H. Identification framework for cracks on a steel structure surface by a restricted boltzmann machines algorithm based on consumer-grade camera images. Struct. Control. Health Monit. 2017, 25, e2075.
  22. Chen, F.-C.; Jahanshahi, M.R. NB-CNN: Deep Learning-Based Crack Detection Using Convolutional Neural Network and Naïve Bayes Data Fusion. IEEE Trans. Ind. Electron. 2018, 65, 4392–4400.
  23. Maeda, H.; Sekimoto, Y.; Seto, T.; Kashiyama, T.; Omata, H. Road Damage Detection and Classification Using Deep Neural Networks with Smartphone Images. Comput.-Aided Civ. Infrastruct. Eng. 2018, 33, 1127–1141.
  24. Zhang, J.; Cai, Y.Y.; Yang, D.; Yuan, Y.; He, W.Y.; Wang, Y.J. Mobilenetv3-BLS: A broad learning approach for automatic concrete surface crack detection. Constr. Build. Mater. 2023, 392, 131941.
  25. Nguyen, C.K.; Kawamura, K.; Nakamura, H. Deep learning-based crack detection and classification for Concrete Structures Inspection. In Proceedings of the 17th East Asian-Pacific Conference on Structural Engineering and Construction, Singapore, 27–30 June 2022; Lecture Notes in Civil Engineering. Springer: Singapore, 2023; pp. 710–717.
  26. Katsigiannis, S.; Seyedzadeh, S.; Agapiou, A.; Ramzan, N. Deep learning for crack detection on masonry façades using limited data and transfer learning. J. Build. Eng. 2023, 76, 107105.
  27. Deng, J.; Lu, Y.; Lee, V.C.-S. Imaging-based crack detection on concrete surfaces using You Only Look Once network. Struct. Health Monit. 2021, 20, 484–499.
  28. Huyan, J.; Li, W.; Tighe, S.; Zhai, J.; Xu, Z.; Chen, Y. Detection of sealed and unsealed cracks with complex backgrounds using deep convolutional neural network. Autom. Constr. 2019, 107, 102946.
  29. Xing, J.; Liu, Y.; Zhang, G.-Z. Improved yolov5-based UAV pavement crack detection. IEEE Sens. J. 2023, 23, 15901–15909.
  30. Chen, D.-R.; Chiu, W.-M. Deep-learning-based road crack detection frameworks for dashcam-captured images under different illumination conditions. Soft Comput. 2023, 27, 14337–14360.
  31. Long, J.; Shelhamer, E.; Darrell, T. Fully convolutional networks for semantic segmentation. In Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA, 7–12 June 2015.
  32. Ronneberger, O.; Fischer, P.; Brox, T. U-Net: Convolutional Networks for Biomedical Image Segmentation. In Medical Image Computing and Computer-Assisted Intervention—MICCAI 2015; Lecture Notes in Computer Science; Springer: Cham, Switzerland, 2015; pp. 234–241.
  33. Yang, X.; Li, H.; Yu, Y.; Luo, X.; Huang, T.; Yang, X. Automatic pixel-level crack detection and measurement using fully convolutional network. Comput.-Aided Civ. Infrastruct. Eng. 2018, 33, 1090–1109.
  34. Bang, S.; Park, S.; Kim, H.; Kim, H. Encoder–decoder network for pixel-level road crack detection in black-box images. Comput.-Aided Civ. Infrastruct. Eng. 2019, 34, 713–727.
  35. Islam, M.M.; Kim, J.-M. Vision-Based Autonomous Crack Detection of Concrete Structures Using a Fully Convolutional Encoder–Decoder Network. Sensors 2019, 19, 4251.
  36. Liu, Z.; Cao, Y.; Wang, Y.; Wang, W. Computer vision-based concrete crack detection using U-net fully convolutional networks. Autom. Constr. 2019, 104, 129–139.
  37. Ji, J.; Wu, L.; Chen, Z.; Yu, J.; Lin, P.; Cheng, S. Automated pixel-level surface crack detection using U-Net. In Multi-Disciplinary Trends in Artificial Intelligence; Lecture Notes in Computer Science; Springer: Cham, Switzerland, 2018; pp. 69–78.
  38. Wang, Y.; Ying, J.; Mao, J.; Chen, Y.; Wu, K. Automatic detection method of bridge cracks based on residual network. IOP Conf. Ser. Earth Environ. Sci. 2021, 643, 012045.
  39. Chen, H.; Lin, H.; Yao, M. Improving the Efficiency of Encoder-Decoder Architecture for Pixel-Level Crack Detection. IEEE Access 2019, 7, 186657–186670.
  40. Sun, M. Semantic Segmentation Using Modified U-Net Architecture for Crack Detection. Master’s Thesis, South Dakota State University, Brookings, SD, USA, 2020.
  41. Lin, F.; Yang, J.; Shu, J.; Scherer, R.J. Crack Semantic Segmentation using the U-Net with Full Attention Strategy. arXiv 2021, arXiv:2104.14586v1.
  42. Augustauskas, R.; Lipnickas, A. Improved pixel-level pavement-defect segmentation using a Deep Autoencoder. Sensors 2020, 20, 2557.
  43. Li, Y.; Ma, R.; Liu, H.; Cheng, G. Real-time high-resolution neural network with semantic guidance for crack segmentation. Autom. Constr. 2023, 156, 105112.
  44. Wang, W.; Su, C.; Han, G.; Zhang, H. A lightweight crack segmentation network based on knowledge distillation. J. Build. Eng. 2023, 76, 107200.
  45. Yang, L.; Huang, H.; Kong, S.; Liu, Y.; Yu, H. PAF-NET: A Progressive and adaptive fusion network for Pavement Crack Segmentation. IEEE Trans. Intell. Transp. Syst. 2023, 24, 12686–12700.
  46. Khan, M.A.-M.; Harseno, R.W.; Kee, S.-H.; Nahid, A.-A. Development of AI- and robotics-assisted automated pavement-crack-evaluation system. Remote Sens. 2023, 15, 3573.
  47. Saha, S.; Karmakar, S.; Manna, D. Analysis of Railroad Track Crack Detection using Computer Vision. In Proceedings of the 2022 Interdisciplinary Research in Technology and Management (IRTM), Kolkata, India, 24–26 February 2022; pp. 1–4.
  48. Fan, H.; Wang, Q.; Luo, Y.; Li, B. Abnormal railway fastener detection using minimal significant regions and local binary patterns. J. Opt. Technol. 2019, 86, 799–807.
  49. Karakose, M.; Yamanand, O.; Murat, K.; Akin, E. A new approach for condition monitoring and detection of rail components and rail track in Railway. Int. J. Comput. Intell. Syst. 2018, 11, 830–845.
  50. Thendral, R.; Ranjeeth, A. Computer Vision System for Railway Track Crack Detection using Deep Learning Neural Network. In Proceedings of the 2021 3rd International Conference on Signal Processing and Communication (ICPSC), Coimbatore, India, 13–14 May 2021; pp. 193–196.
  51. Min, Y.; Xiao, B.; Dang, J.; Yue, B.; Cheng, T. Real Time Detection System for Rail Surface Defects Based on Machine Vision. EURASIP J. Image Video Process. 2018, 2018, 3.
  52. Mohammad, S.P. Machine Vision for Automating Visual Inspection of Wooden Sleepers. Master’s Thesis, DALARNA University, Borlange, Sweden, 2008.
  53. Tabatabaei, S.A.; Delforouzi, A.; Khan, M.H.; Wesener, T.; Grzegorzek, M. Automatic detection of the cracks on the concrete railway sleepers. Int. J. Pattern Recognit. Artif. Intell. 2019, 33, 1955010.
  54. Kim, M.; Kim, K.; Choi, S. Development of automatic crack identification algorithm for a concrete sleeper using pattern recognition. J. Korean Soc. Railw. 2017, 20, 374–381.
  55. Wang, G.; Liu, Y.; Xiang, J. A two-stage algorithm of railway sleeper crack detection based on edge detection and CNN. In Proceedings of the 2020 Asia-Pacific International Symposium on Advanced Reliability and Maintenance Modeling (APARM), Vancouver, BC, Canada, 20–23 August 2020.
  56. Xia, B.; Cao, J.; Zhang, X.; Peng, Y. Automatic Concrete Sleeper Crack Detection using a one-stage detector. Int. J. Intell. Robot. Appl. 2020, 4, 319–327.
  57. Jang, J.; Shin, M.; Lim, S.; Park, J.; Kim, J.; Paik, J. Intelligent image-based railway inspection system using Deep Learning-based object detection and Weber contrast-based image comparison. Sensors 2019, 19, 4738.
Video Production Service