MRSDI-CNN: Multi-Model Rail Surface Defect Inspection System Based on Convolutional Neural Networks

Zhang, Hui; Song, Yong; Chen, Yurong; Zhong, Huicai; Liu, Li; Akilan, Thangarajah; Wu, Qi

doi:10.1109/tits.2021.3101053

Cited by 45 publications

(10 citation statements)

References 49 publications

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“…The primary objective of the training was to optimize model performance by minimizing the loss function. The bounding box regression loss typically employs mean squared error (MSE) for direct regression on the bounding's box coordinates, height, and width [46], with binary cross-entropy loss applied to analyze confidence and classification losses. Compared to early YOLO, YOLOV5 innovates in bounding box regression by replacing MSE with CIOU [47].…”

Section: Hyperparametersmentioning

confidence: 99%

Automated Identification and Localization of Rail Internal Defects Based on Object Detection Networks

Wang,

Yan,

et al. 2024

Applied Sciences

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The timely identification of rail internal defects and the application of corresponding preventive measures would greatly reduce catastrophic failures, such as rail breakage. Ultrasonic rail defect detection is the current mainstream rail defect detection method thanks to its advantages of strong penetration, high accuracy, and ease to deploy. The 2D B-scan image output by ultrasonic detectors contains rich features of defects; however, rail engineers manually identify and localize the defect image, which can be time-consuming, and the image may be subject to missing identification or mistakes. This paper adopted state-of-the-art deep learning algorithms for novel B-scan images for the automatic identification and localization of rail internal tracks. First, through image pre-processing of classification, denoising, and augmentation, four categories of defect image datasets were established, namely crescent-shaped fatigue cracks, fishbolt hole cracks, rail web cracks, and rail base transverse cracks; then, four representatives of deep learning object detection networks, YOLOv8, YOLOv5, DETR, and Faster R-CNN, were trained with the defects dataset and further applied to the testing dataset for defect identification; finally, the performances of the three detection networks were compared and evaluated at the data level, the network structure level, and the interference adaptability level, respectively. The results show that the YOLOv8 network can effectively classify and localize four categories of internal rail defects in B-scan images with a 93.3% mean average precision at three images per second, and the detection time is 58.9%, 376.8%, and 123.2% faster than YOLO v5, DETR, and Faster R-CNN, respectively. The proposed approach could ensure the real-time, accurate, and efficient detection and analysis of internal defects to a rail.

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Section: Hyperparametersmentioning

confidence: 99%

Automated Identification and Localization of Rail Internal Defects Based on Object Detection Networks

Wang,

Yan,

et al. 2024

Applied Sciences

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“…Meng Si et al proposed a multi-task architecture for rail surface defect detection, which includes two branch models for rail detection and defect segmentation [ 17 ]. Zhang Hui et al cascaded the one-stage object detection algorithms SSD and YOLOv3, integrating the detection results from both networks to improve the accuracy of rail surface defect detection [ 18 ]. However, these approaches neglected the fact that defect samples are scarce and difficult to obtain in practical work.…”

Section: Related Workmentioning

confidence: 99%

FS-RSDD: Few-Shot Rail Surface Defect Detection with Prototype Learning

Min,

Wang,

Liu

et al. 2023

Sensors

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As an important component of the railway system, the surface damage that occurs on the rails due to daily operations can pose significant safety hazards. This paper proposes a simple yet effective rail surface defect detection model, FS-RSDD, for rail surface condition monitoring, which also aims to address the issue of insufficient defect samples faced by previous detection models. The model utilizes a pre-trained model to extract deep features of both normal rail samples and defect samples. Subsequently, an unsupervised learning method is employed to learn feature distributions and obtain a feature prototype memory bank. Using prototype learning techniques, FS-RSDD estimates the probability of a test sample belonging to a defect at each pixel based on the prototype memory bank. This approach overcomes the limitations of deep learning algorithms based on supervised learning techniques, which often suffer from insufficient training samples and low credibility in validation. FS-RSDD achieves high accuracy in defect detection and localization with only a small number of defect samples used for training. Surpassing benchmarked few-shot industrial defect detection algorithms, FS-RSDD achieves an ROC of 95.2% and 99.1% on RSDDS Type-I and Type-II rail defect data, respectively, and is on par with state-of-the-art unsupervised anomaly detection algorithms.

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“…The model successfully improved the detection accuracy. Zhang et al [65] used the improved YOLOV3 and the improved SSD for parallel prediction and fused the prediction results of the two to enhance the accuracy of prediction. Although this can improve the final detection accuracy, it consumes much more computing resources.…”

Section: Related Workmentioning

confidence: 99%

An Efficient End-to-End Multitask Network Architecture for Defect Inspection

Zhang

Yang

et al. 2022

Sensors

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Recently, computer vision-based methods have been successfully applied in many industrial fields. Nevertheless, automated detection of steel surface defects remains a challenge due to the complexity of surface defects. To solve this problem, many models have been proposed, but these models are not good enough to detect all defects. After analyzing the previous research, we believe that the single-task network cannot fully meet the actual detection needs owing to its own characteristics. To address this problem, an end-to-end multi-task network has been proposed. It consists of one encoder and two decoders. The encoder is used for feature extraction, and the two decoders are used for object detection and semantic segmentation, respectively. In an effort to deal with the challenge of changing defect scales, we propose the Depthwise Separable Atrous Spatial Pyramid Pooling module. This module can obtain dense multi-scale features at a very low computational cost. After that, Residually Connected Depthwise Separable Atrous Convolutional Blocks are used to extract spatial information under low computation for better segmentation prediction. Furthermore, we investigate the impact of training strategies on network performance. The performance of the network can be optimized by adopting the strategy of training the segmentation task first and using the deep supervision training method. At length, the advantages of object detection and semantic segmentation are tactfully combined. Our model achieves mIOU 79.37% and mAP@0.5 78.38% on the NEU dataset. Comparative experiments demonstrate that this method has apparent advantages over other models. Meanwhile, the speed of detection amount to 85.6 FPS on a single GPU, which is acceptable in the practical detection process.

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MRSDI-CNN: Multi-Model Rail Surface Defect Inspection System Based on Convolutional Neural Networks

Cited by 45 publications

References 49 publications

Automated Identification and Localization of Rail Internal Defects Based on Object Detection Networks

Automated Identification and Localization of Rail Internal Defects Based on Object Detection Networks

FS-RSDD: Few-Shot Rail Surface Defect Detection with Prototype Learning

An Efficient End-to-End Multitask Network Architecture for Defect Inspection

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