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Xu, X.; Zhang, G.; Zheng, W.; Zhao, A.; Zhong, Y.; Wang, H. High-Precision Detection Algorithm for Metal Workpiece Defects. Encyclopedia. Available online: https://encyclopedia.pub/entry/48816 (accessed on 23 June 2024).
Xu X, Zhang G, Zheng W, Zhao A, Zhong Y, Wang H. High-Precision Detection Algorithm for Metal Workpiece Defects. Encyclopedia. Available at: https://encyclopedia.pub/entry/48816. Accessed June 23, 2024.
Xu, Xiujin, Gengming Zhang, Wenhe Zheng, Anbang Zhao, Yi Zhong, Hongjun Wang. "High-Precision Detection Algorithm for Metal Workpiece Defects" Encyclopedia, https://encyclopedia.pub/entry/48816 (accessed June 23, 2024).
Xu, X., Zhang, G., Zheng, W., Zhao, A., Zhong, Y., & Wang, H. (2023, September 05). High-Precision Detection Algorithm for Metal Workpiece Defects. In Encyclopedia. https://encyclopedia.pub/entry/48816
Xu, Xiujin, et al. "High-Precision Detection Algorithm for Metal Workpiece Defects." Encyclopedia. Web. 05 September, 2023.
High-Precision Detection Algorithm for Metal Workpiece Defects
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Computer vision technology is increasingly being widely applied in automated industrial production. The development of machine-vision-based automated inspection methods has overcome the limitations of low accuracy, poor real-time performance, and high labor intensity associated with manual inspection. This technology has emerged as a fast and reliable alternative for detecting various surface defects, offering significant advantages such as high automation, reliability, and objectivity.

neural networks defect detection

1. Introduction

In complex industrial production processes, defects such as collision damage, dents, wear, scratches, etc., can occur due to design and mechanical equipment failures, adverse working conditions, or human factors. In everyday use, products are also prone to corrosion and fatigue. These defects of different degrees increase the cost for companies, shorten the lifespan of products, and result in significant waste of resources, posing great risks to personal safety and social–economic development. Therefore, the capability of defect detection is the key to improving product quality without compromising production efficiency. In recent years, the development of machine-vision-based automated inspection methods has overcome the limitations of low accuracy, poor real-time performance, and high labor intensity associated with manual inspection [1]. This technology has emerged as a fast and reliable alternative for detecting various surface defects, offering significant advantages such as high automation, reliability, and objectivity. Machine-vision-based inspection has demonstrated strong adaptability to different environmental conditions and can operate continuously with high levels of precision and efficiency [2]. However, industrial visual defect detection methods are required to be equipped with characteristics such as high precision, high efficiency, and low cost [3]. This suggests that these requirements have also become important bottlenecks in the field of computer vision detection [4].
With the rapid development of deep learning technology, significant successes have been achieved in various fields such as object detection [5], intelligent robotics [6], saliency detection [7], parking-lot sound event detection [8], smart-city safety sound event detection [9][10], and drone-blade fault diagnosis [11][12][13][14]. Deep learning is a machine learning method that uses multi-layer neural networks for automatic feature learning and pattern recognition. By combining low-level features to form higher-level representations of abstract attribute categories or features, deep learning algorithms can realize more accurate data understanding and analyses in terms of edges, shapes, and other abstract characteristics, thereby strengthening their effectiveness. As a result, numerous researchers have been exploring the utilization of deep learning techniques for defect detection in products to enhance product quality and production efficiency [15][16][17][18].

2. High-Precision Detection Algorithm for Metal Workpiece Defects

Industrial defect detection has consistently remained as a prominent research topic undertaken within the realm of industrial vision. Machine vision algorithms offer a diverse range of methods for detecting defects, broadly classified into two categories: conventional approaches and deep learning methodologies. Cheetverikov et al. [19] effectively utilized these techniques to detect sudden flaws on fabric surfaces, where texture defects were analyzed by employing two fundamental structural characteristics, specifically consistency and local direction (anisotropy). Hou et al. [20] showcased that the precise recognition and partitioning of defects exposed on the surface of textures can be attained through the utilization of support vector machine classification methods relying on Gabor wavelet characteristics. Zheng et al. [21] proposed a VMD modulus optimization method based on maximum envelope kurtosis, which exhibits strong generalization and noise resistance. Cha et al. [22] introduced a visual detection technique for structures, with the help of the Faster Region Convolutional Neural Network (Faster RCNN) [23]. This method enables simultaneous and near-real-time identification of concrete fractures, medium- and high-level steel erosion, bolt erosion, as well as five distinct forms of steel delamination harm. With a resolution of 500 × 375, this approach provides a relatively rapid speed of detection, averaging 0.03 s per image. However, industrial vision defect detection requires highly accurate methods capable of detecting subtle defects that are difficult for the human eye to observe. It is not only necessary to minimize false negatives and positives but also to be able to adjust the detection performance in a timely manner. According to the aforementioned literature, traditional methods are only suitable for defects with specific geometric features. Traditional methods require the manual design of feature descriptors for defects, which are appropriate for simple and rule-based industrial scenarios. The subtle nature and subjectivity of defects pose great challenges in the accurate description of defects using manual features. Moreover, due to the unknown and diverse nature of defects, multiple sets of defect templates need to be designed, but they cannot detect novel defects, only describing a limited range of defect types. When faced with complex and irregular data, traditional methods not only struggle to be applied but may also require complex post-processing procedures [3]. In most cases, on account of the complexity of mechanical processing, the types of defects in workpieces are diverse and traditional methods are no longer competent for defect detection.
In recent years, with the widespread application of deep learning in computer vision tasks [24][25][26][27][28][29][30], deep-learning-based industrial defect detection methods have rapidly developed and gradually become the mainstream. Due to the powerful feature extraction and representation capabilities of convolutional neural networks (CNN) for high-dimensional data, deep-learning-based methods can achieve the automatic learning of features that are difficult for humans to design. This not only saves the cost of manually designing features but also significantly improves detection accuracy. Compared to traditional methods based on image processing and statistical learning, deep learning methods present advantages in handling complex industrial image data. Tao et al. [31] designed a novel cascaded autoencoder (CASAE) architecture for defect segmentation and localization. This approach satisfies the criteria for robustness and precision in identifying defects in metal materials. However, it is impractical for defects such as bad stuff and freckles owing to large numbers of labels needed, and a significant amount of manual labor required for creating and analyzing the dataset. Gao et al. [32] proposed a convolutional neural network with feature alignment trained in a hierarchical manner. The method introduces feature alignment, which maps unrecognizable defects to recognizable areas, and incorporates feature alignment into the training process using a hierarchical training strategy. Nevertheless, the network still needs improvement in recognizing small defects. Yoon et al. [33] presented a technique for real-time non-destructive testing for layered composite material defects by virtue of highly nonlinear solitary waves (HNSWs) in deep learning. The accuracy level of this technique exceeds 90%, highlighting the potential of real-time detection utilizing the proposed deep learning algorithm. However, this method is restricted to the layered detection of AS4/PEEK laminated composite materials, which has significant limitations. Wang et al. [34] put forward an unsupervised surface defect detection method based on a non-convex total variation (TV)-regularized kernelized Robust Principal Component Analysis (RPCA). However, this method is prone to false negatives in terms of detecting small-sized defects and requires further improvement. Yang et al. [35] came up with an effective unsupervised anomaly segmentation method that can detect and segment anomalies in small regions and constrained areas of an image. They designed a deep and efficient convolutional autoencoder to detect abnormal regions in images through fast feature reconstruction. However, this method struggles to detect those defect types without concave or convex surface features. Yang et al. [36] proposed a new method called a Multi-Scale Feature-Clustering-based Fully Convolutional Autoencoder (MS-FCAE) for the efficient and accurate detection of various types of texture defects with a small number of defect-free texture samples. But, this method is only applicable to different types of texture defects and lacks generalizability. Li et al. [37] introduced a novel automatic defect detection approach based on deep learning, namely You Only Look Once (YOLO)-Attention based on YOLOv4, which achieved fast and accurate defect detection in Web-based Augmented Assembly Manufacturing (WAAM). Wang et al. [38] presented an accurate object detector, ATT-YOLO (Attention-YOLO), which is oriented toward the problem of surface defect detection in electronics manufacturing. ATT-YOLO satisfies the requirements of surface defect detection and achieves the best tradeoff among lightweight YOLO-style object detectors.
In summary, there are various methods for detecting defects in metal workpieces, but the drawbacks of traditional methods are evident. Deep learning methods can be classified into two major categories: defect segmentation and object detection. However, based on the extensive literature reviewed, defect segmentation methods lack generalizability and are unable to achieve sufficient accuracy for detecting small-sized defects. On the other hand, object detection methods exhibit strong generalizability, an ease of dataset creation, high accuracy, and efficiency.

References

  1. Rasheed, A.; Zafar, B.; Rasheed, A.; Ali, N.; Sajid, M.; Dar, S.H.; Habib, U.; Shehryar, T.; Mahmood, M.T. Fabric Defect Detection Using Computer Vision Techniques: A Comprehensive Review. Math. Probl. Eng. 2020, 2020, 8189403.
  2. Yang, J.; Li, S.; Wang, Z.; Dong, H.; Wang, J.; Tang, S. Using Deep Learning to Detect Defects in Manufacturing: A Comprehensive Survey and Current Challenges. Materials 2020, 13, 5755.
  3. Chen, Y.; Ding, Y.; Zhao, F.; Zhang, E.; Wu, Z.; Shao, L. Surface Defect Detection Methods for Industrial Products: A Review. Appl. Sci. 2021, 11, 7657.
  4. Zou, Z.; Chen, K.; Shi, Z.; Guo, Y.; Ye, J. Object Detection in 20 Years: A Survey. Proc. IEEE 2023, 111, 257–276.
  5. Khan, F.; Salahuddin, S.; Javidnia, H. Deep Learning-Based Monocular Depth Estimation Methods—A State-of-the-Art Review. Sensors 2020, 20, 2272.
  6. Yang, G.; Yang, J.; Sheng, W.; Junior, F.E.F.; Li, S. Convolutional Neural Network-Based Embarrassing Situation Detection under Camera for Social Robot in Smart Homes. Sensors 2018, 18, 1530.
  7. Borji, A.; Cheng, M.; Jiang, H.; Li, J. Salient Object Detection: A Benchmark. IEEE Trans. Image Process. 2015, 24, 5706–5722.
  8. Ciaburro, G. Sound Event Detection in Underground Parking Garage Using Convolutional Neural Network. Big Data Cogn. Comput. 2020, 4, 20.
  9. Ciaburro, G.; Iannace, G. Improving Smart Cities Safety Using Sound Events Detection Based on Deep Neural Network Algorithms. Informatics 2020, 7, 23.
  10. Costa, D.G.; Vasques, F.; Portugal, P.; Aguiar, A. A Distributed Multi-Tier Emergency Alerting System Exploiting Sensors-Based Event Detection to Support Smart City Applications. Sensors 2020, 20, 170.
  11. Iannace, G.; Ciaburro, G.; Trematerra, A. Fault Diagnosis for UAV Blades Using Artificial Neural Network. Robotics 2019, 8, 59.
  12. Peng, L.; Liu, J. Detection and analysis of large-scale WT blade surface cracks based on UAV-taken images. IET Image Process. 2018, 12, 2059–2064.
  13. Saied, M.; Lussier, B.; Fantoni, I.; Shraim, H.; Francis, C. Fault Diagnosis and Fault-Tolerant Control of an Octorotor UAV using motors speeds measurements. IFAC-PapersOnLine 2017, 50, 5263–5268.
  14. Deng, L.; Yu, D. Deep learning: Methods and applications. Found. Trends Signal Process. 2014, 7, 197–387.
  15. Tao, X.; Wang, Z.; Zhang, Z.; Zhang, D.; Xu, D.; Gong, X.; Zhang, L. Wire Defect Recognition of Spring-Wire Socket Using Multitask Convolutional Neural Networks. IEEE Trans. Compon. Packag. Manuf. Technol. 2018, 8, 689–698.
  16. Jiang, J.; Chen, Z.; He, K. A feature-based method of rapidly detecting global exact symmetries in CAD models. Comput. Aided Des. 2013, 45, 1081–1094.
  17. Cheng, J.C.P.; Wang, M. Automated detection of sewer pipe defects in closed-circuit television images using deep learning techniques. Autom. Constr. 2018, 95, 155–171.
  18. Bergmann, P.; Löwe, S.; Fauser, M.; Sattlegger, D.; Steger, C. Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. arXiv 2019, arXiv:1807.02011.
  19. Chetverikov, D.; Hanbury, A. Finding defects in texture using regularity and local orientation. Pattern Recogn. 2002, 35, 2165–2180.
  20. Hou, Z.; Parker, J.M. Texture Defect Detection Using Support Vector Machines with Adaptive Gabor Wavelet Features. In Proceedings of the 2005 Seventh IEEE Workshops on Applications of Computer Vision (WACV/MOTION’05)-Volume 1, Breckenridge, CO, USA, 5–7 January 2005; pp. 275–280.
  21. Zheng, S.; Zhong, Q.; Chen, X.; Peng, L.; Cui, G. The Rail Surface Defects Recognition via Operating Service Rail Vehicle Vibrations. Machines 2022, 10, 796.
  22. Cha, Y.; Choi, W.; Suh, G.; Mahmoudkhani, S.; Büyüköztürk, O. Autonomous Structural Visual Inspection Using Region-Based Deep Learning for Detecting Multiple Damage Types. Comput.-Aided Civ. Infrastruct. Eng. 2018, 33, 731–747.
  23. Ren, S.; He, K.; Girshick, R.; Sun, J. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. IEEE Trans. Pattern Anal. 2017, 39, 1137–1149.
  24. Zhang, Y.; Cai, W.; Fan, S.; Song, R.; Jin, J. Object Detection Based on YOLOv5 and GhostNet for Orchard Pests. Information 2022, 13, 548.
  25. Ye, W.; Ren, J.; Zhang, A.A.; Lu, C. Automatic pixel-level crack detection with multi-scale feature fusion for slab tracks. Comput.-Aided Civ. Infrastruct. Eng. 2023, 1–18.
  26. Zhang, W.; Liu, W.; Li, L.; Jiao, H.; Li, Y.; Guo, L.; Xu, J. A framework for the efficient enhancement of non-uniform illumination underwater image using convolution neural network. Comput. Graph. 2023, 112, 60–71.
  27. Qiao, Y.; Shao, M.; Liu, H.; Shang, K. Mutual channel prior guided dual-domain interaction network for single image raindrop removal. Comput. Graph. 2023, 112, 132–142.
  28. Koulali, I.; Eskil, M.T. Unsupervised textile defect detection using convolutional neural networks. Appl. Soft Comput. 2021, 113, 107913.
  29. Wang, J.; Yu, L.; Yang, J.; Dong, H. DBA_SSD: A Novel End-to-End Object Detection Algorithm Applied to Plant Disease Detection. Information 2021, 12, 474.
  30. Panboonyuen, T.; Thongbai, S.; Wongweeranimit, W.; Santitamnont, P.; Suphan, K.; Charoenphon, C. Object Detection of Road Assets Using Transformer-Based YOLOX with Feature Pyramid Decoder on Thai Highway Panorama. Information 2022, 13, 5.
  31. Tao, X.; Zhang, D.; Ma, W.; Liu, X.; Xu, D. Automatic Metallic Surface Defect Detection and Recognition with Convolutional Neural Networks. Appl. Sci. 2018, 8, 1575.
  32. Gao, Y.; Gao, L.; Li, X. A hierarchical training-convolutional neural network with feature alignment for steel surface defect recognition. Robot. Comput.-Integr. Manuf. 2023, 81, 102507.
  33. Yoon, S.; Song-Kyoo Kim, A.; Cantwell, W.J.; Yeun, C.Y.; Cho, C.; Byon, Y.; Kim, T. Defect detection in composites by deep learning using solitary waves. Int. J. Mech. Sci. 2023, 239, 107882.
  34. Wang, J.; Xu, G.; Li, C.; Wang, Z.; Yan, F. Surface Defects Detection Using Non-convex Total Variation Regularized RPCA With Kernelization. IEEE Trans. Instrum. Meas. 2021, 70, 1–13.
  35. Yang, J.; Shi, Y.; Qi, Z. DFR: Deep Feature Reconstruction for Unsupervised Anomaly Segmentation. arXiv 2020, arXiv:2012.07122.
  36. Yang, H.; Chen, Y.; Song, K.; Yin, Z. Multiscale Feature-Clustering-Based Fully Convolutional Autoencoder for Fast Accurate Visual Inspection of Texture Surface Defects. IEEE Trans. Autom. Sci. Eng. 2019, 16, 1450–1467.
  37. Li, W.; Zhang, H.; Wang, G.; Xiong, G.; Zhao, M.; Li, G.; Li, R. Deep learning based online metallic surface defect detection method for wire and arc additive manufacturing. Robot. Comput.-Integr. Manuf. 2023, 80, 102470.
  38. Wang, J.; Dai, H.; Chen, T.; Liu, H.; Zhang, X.; Zhong, Q.; Lu, R. Toward surface defect detection in electronics manufacturing by an accurate and lightweight YOLO-style object detector. Sci. Rep. 2023, 13, 7062.
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