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An adaptive clustering method detecting the surface defects on linear guide rails
International Journal of Computer Integrated Manufacturing, 2019Linear guides are widely used in automation, aerospace production, and medical care. It is currently one of the most concerning issues how to monitor its surface quality in the manufacturing proces...
Youhang Zhou +3 more
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Detection of rail surface defects based on CNN image recognition and classification
2018 20th International Conference on Advanced Communication Technology (ICACT), 2018Due to the rapid advances in railway industry, the rail surface defect detection task which inspects whether the rail is defective has become an increasingly critical issue. Detecting rails by an automatic and swift approach instead of present manual inspections enables the work more efficient and safe currently.
Lidan Shang +4 more
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Rail Surface Defects Detection Based on Faster R-CNN
2020 International Conference on Artificial Intelligence and Electromechanical Automation (AIEA), 2020Rail surface defects detection is very important for improving railway safety. Therefore, we use Faster R-CNN to conduct research on the rail surface defects detection. Firstly, a data set of rail surface defect images is established, and then the training set of random segmentation is used for effective training to a certain extent, and the ...
Xiaobo Chen, Huimin Zhang
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Simulation of Laser Ultrasonics for Detection of Surface-Connected Rail Defects
Journal of Nondestructive Evaluation, 2017Laser ultrasonic produces frequencies in the MHz range, enabling high accuracy and a strong ability to detect rail surface defects. This paper mainly studied on the simulation of detecting surface-connected rail defects on 60 kg rails with laser ultrasonic, established the finite element model of laser-excited ultrasonic Rayleigh wave, carried out the ...
Zhong Yunjie +4 more
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Detection of Critical Defects in Rails Using Ultrasonic Surface Waves
AIP Conference Proceedings, 2007Defects in rails caused by rolling contact fatigue (RCP) are of growing concern to the railway industry. Conventional ultrasonic inspection methods are often not reliable in detecting critical RCF defects. The aim of this work was therefore to develop a reliable screening tool that discriminates between critical and tolerable defects and therefore ...
D. Hesse, P. Cawley
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Rail Defect Detection Using Ultrasonic Surface Waves
AIP Conference Proceedings, 2006Current testing of the rail network is limited in terms of both speed of testing and accuracy of detecting surface defects such as gauge corner cracking. By using ultrasonic surface waves generated and detected in a pitch‐catch manner we can detect such defects with a much higher accuracy.
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Defect Detection of Rail Surface with Deep Convolutional Neural Networks
2018 13th World Congress on Intelligent Control and Automation (WCICA), 2018With the heavy railway transportation pressure, the rail surface defect that came into being is an unavoidable problem, which is related to the railway transport safety. Therefore various defect detection methods of rail surface are proposed, but the accuracy, rapidity, stability and intelligence are still unsatisfactory. To overcome these difficulties,
Zhicong Liang +4 more
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Rail Surface Defect Detection and Localisation System
SSRN Electronic Journal, 2023Binsiya C, Baburaj M
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Rail surface defect detection based on improved YOLOv8n
Engineering Research ExpressAbstract Rail surface defects such as scratches, spalling, cracks, wear, corrugation, and depressions pose serious risks to railway safety and require fast, reliable inspection. In practice, defect images often exhibit low contrast, strong texture noise, and large scale variations. These issues cause missed detections and false alarms,
Li Song, Dexin Song
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A Deep Convolutional Neural Network for Detection of Rail Surface Defect
2019 IEEE Vehicle Power and Propulsion Conference (VPPC), 2019Railway surface defect detection is an important technical measure to ensure the safe operation of the rail transit system. Due to the complex and diverse features of rail surface defects and the uneven curvature of the image caused by the track surface, it is difficult to obtain better detection results by traditional machine vision technology.
Hao Yuan +4 more
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