Thresholding Based on Maximum Weighted Object Correlation for Rail Defect Detection

Li, Qingyong; Huang, Yaping; Liang, Zhengping; Luo, Siwei

doi:10.1587/transinf.e95.d.1819

Cited by 2 publications

(2 citation statements)

References 12 publications

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“…Machine vision detection mainly focuses on improving image quality by performing various computational steps to transform the image into a surface that highlights certain pieces of interesting information. These steps include image contrast enhancement [ 12 , 13 , 14 ], image filtering [ 15 , 16 ], and edge extraction [ 17 , 18 ]. When it comes to analyzing crack images, it is usually difficult to detect and extract the targets due to the presence of interference from noise and other problems.…”

Section: Related Research and Methodsmentioning

confidence: 99%

Optical Rail Surface Crack Detection Method Based on Semantic Segmentation Replacement for Magnetic Particle Inspection

Kou

Sysyn²,

Fischer³

et al. 2022

Sensors

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Railway damage detection is of great significance in ensuring railway safety. The cracks on the rail surface play a key role in studying the formation and development process of rail damage, predicting the occurrence of rail defects, and then improving the service life of the rail. However, due to the small shape of the cracks, the typical detection method is relatively complicated, and the speed is quite slow. Although traditional magnetic particle inspection technology is fairly accurate at detection, it is costly and inconvenient to carry and install, while also limiting the detection speed and affecting the system’s operation. In this paper, a semantic segmentation detection method is developed by using various collected rail surface crack data and deep learning through a neural network. By comparing the inspection of the same rail surface with magnetic particle inspection technology, only inexpensive cameras are used and the inspection speed is increased while maintaining relatively high accuracy. In addition, the method can achieve fast detection speeds if it is extended to be combined with high-frequency cameras. It is an economical, efficient, and environmentally friendly method for future rail surface detection.

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Section: Related Research and Methodsmentioning

confidence: 99%

Optical Rail Surface Crack Detection Method Based on Semantic Segmentation Replacement for Magnetic Particle Inspection

Kou

Sysyn²,

Fischer³

et al. 2022

Sensors

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show abstract

“…However, this detection method is susceptible to noise, and the recall performance of the defect detection system needs to be improved. In [26]- [28], image enhancement combined with threshold binarization is adopted to detect discrete defects. Some attempts have been made to improve detection performance, but irregularities seriously influence such methods, and threshold selection is not universal.…”

Section: Introductionmentioning

confidence: 99%

Multi-Target Defect Identification for Railway Track Line Based on Image Processing and Improved YOLOv3 Model

et al. 2020

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The condition monitoring of railway track line is one of the essential tasks to ensure the safety of the railway transportation system. Railway track line is mainly composed of tracks, fasteners, sleepers, and so on. Given the requirements for rapid and accurate inspection, innovative and intelligent methods for multi-target defect identification of the railway track line using image processing and deep learning methods are proposed in this paper. Firstly, the track and fastener positioning method based on variance projection and wavelet transform is introduced. After that, a bag-of-visual-word (BOVW) model combined with spatial pyramid decomposition is proposed for railway track line multi-target defect detection with a detection accuracy of 96.26%. Secondly, an improved YOLOv3 model named TLMDDNet (Track Line Multi-target Defect Detection Network), integrating scale reduction and feature concatenation, is proposed to enhance detection accuracy and efficiency. Finally, to reduce model complexity and further improve the detection speed, with the help of dense connection structure, a lightweight design strategy for the TLMDDNet model named DC-TLMDDNet (Dense Connection Based TLMDDNet) is proposed, in which the DenseNet is applied to optimize feature extraction layers in the backbone network of TLMDDNet. The effectiveness of the proposed methods is demonstrated by the experimental results. INDEX TERMS Railway track line defects, multi-target defect identification, image processing, deep learning, YOLOv3.

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Thresholding Based on Maximum Weighted Object Correlation for Rail Defect Detection

Cited by 2 publications

References 12 publications

Optical Rail Surface Crack Detection Method Based on Semantic Segmentation Replacement for Magnetic Particle Inspection

Optical Rail Surface Crack Detection Method Based on Semantic Segmentation Replacement for Magnetic Particle Inspection

Multi-Target Defect Identification for Railway Track Line Based on Image Processing and Improved YOLOv3 Model

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