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Xulong Yu, Qiancheng Yu, Qunyue Mu, Zhiyong Hu and Jincai Xie
Traditional manual visual detection methods are inefficient, subjective, and costly, making them prone to false and missed detections. Deep-learning-based defect detection identifies the types of defects and pinpoints their locations. By employing this a...
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Haotao Wang, Haijun Zhang, Ming Zhou, Chengbo Gu, Sutong Bai and Hao Lin
SiCp/Al composites are used in the aerospace, automotive, and electronics fields, among others, due to their excellent physical and mechanical properties. However, as they are hard-to-machine materials, poor surface quality has become a major limitation ...
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Alireza Saberironaghi, Jing Ren and Moustafa El-Gindy
Over the last few decades, detecting surface defects has attracted significant attention as a challenging task. There are specific classes of problems that can be solved using traditional image processing techniques. However, these techniques struggle wi...
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Jiatong Hou, Bo You, Jiazhong Xu, Tao Wang and Moran Cao
This paper proposes a lightweight detection model based on machine vision, YOLOv5-GC, to improve the efficiency and accuracy of detecting and classifying surface defects in preforming materials. During this process, clear images of the entire surface are...
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Hui Luo, Lianming Cai and Chenbiao Li
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 detect...
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