Results 1 to 10 of about 1,151 (212)
A 3D Laser Profiling System for Rail Surface Defect Detection. [PDF]
Sensors (Basel), 2017 Rail surface defects such as the abrasion, scratch and peeling often cause damages to the train wheels and rail bearings. An efficient and accurate detection of rail defects is of vital importance for the safety of railway transportation. In the past few decades, automatic rail defect detection has been studied; however, most developed methods use ...
Xiong Z, Li Q, Mao Q, Zou Q.
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RSDNet: A New Multiscale Rail Surface Defect Detection Model. [PDF]
Sensors (Basel)The rapid and accurate identification of rail surface defects is critical to the maintenance and operational safety of the rail. For the problems of large-scale differences in rail surface defects and many small-scale defects, this paper proposes a rail surface defect detection algorithm, RSDNet (Rail Surface Defect Detection Net), with YOLOv8n as the ...
Du J, Zhang R, Gao R, Nan L, Bao Y.
europepmc +8 more sources
FS-RSDD: Few-Shot Rail Surface Defect Detection with Prototype Learning. [PDF]
Sensors (Basel), 2023 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.
Min Y, Wang Z, Liu Y, Wang Z.
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Rail Surface Defect Detection Based on An Improved YOLOv5s
Applied Sciences, 2023 As the operational time of the railway increases, rail surfaces undergo irreversible defects. Once the defects occur, it is easy for them to develop rapidly, which seriously threatens the safe operation of trains. Therefore, the accurate and rapid detection of rail surface defects is very important.
Hui Luo, Lianming Cai, Chenbiao Li
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Ensemble model for rail surface defects detection
PLOS ONE, 2022 The detection of rail surface defects is vital for high-speed rail maintenance and management. The CNN-based computer vision approach has been proved to be a strong detection tool widely used in various industrial scenarios. However, the CNN-based detection models are diverse from each other in performance, and most of them require sufficient training ...
Hailang Li +5 more
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An Improved Feature Pyramid Network and Metric Learning Approach for Rail Surface Defect Detection
Applied Sciences, 2023 When deep learning methods are used to detect rail surface defects, the training accuracy declines due to small defects and an insufficient number of samples.
Zhendong He +4 more
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An Improved YOLOv8 Algorithm for Rail Surface Defect Detection
IEEE AccessTo tackle the issues raised by detecting small targets and densely occluded targets in railroad track surface defect detection, we present an algorithm for detecting defects on railroad tracks based on the YOLOv8 model. Firstly, we enhance the model’s attention towards small and medium-sized targets by substituting replacing the original ...
Yan Wang +3 more
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Enhanced Rail Surface Defect Segmentation Using Polarization Imaging and Dual-Stream Feature Fusion. [PDF]
Sensors (Basel)Rail surface defects pose significant risks to the operational efficiency and safety of industrial equipment. Traditional visual defect detection methods typically rely on high-quality RGB images; however, they struggle in low-light conditions due to ...
Pan Y +5 more
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Railway Wheel Flat and Rail Surface Defect Detection by Time-Frequency Analysis
Chemical Engineering Transactions, 2013 Damage to the surface of railway wheels and rails commonly occurs in most railways and, if not detected at an early stage, can result in rapid deterioration and possible failure incurring high maintenance costs. If detected at an early stage these maintenance costs can be minimised.
Liang, Bo +5 more
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Correction: Ensemble model for rail surface defects detection
PLOS ONE, 2023 [This corrects the article DOI: 10.1371/journal.pone.0268518.].
Hailang Li +5 more
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