A real-time defect detection in printed circuit boards applying deep learning
نویسندگان
چکیده
Inspection of defects in the printed circuit boards (PCBs) has both safety and economic significance 4.0 industrial manufacturing. Nevertheless, it is still a challenging problem to be studied in-depth due complexity PCB layouts shrinking down tendency electronic component size. In this paper, real-time automated supervision algorithm proposed test PCBs quality among different scenarios. The density layout on surface are analyzed based deep learning image feature extraction algorithms. To more detailed, ORB Brute-force matching method utilized match perfectly input images with templates. After transferring by aiding RANSAC algorithm, hybrid using modern computer vision algorithms developed segment defective areas surface. Then, applying enhanced Residual Network –50, can classify groove mount technology components which minimum size up 1x3 mm. training process, system capable categorize various types overproduced, recycled, cloned PCBs. speed testing operation maintains at high level an average precision rate 96.29 % case good brightness conditions. Finally, computational experiments demonstrate that obtain superior results outperforms several existing works terms speed, precision, robustness
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ژورنال
عنوان ژورنال: Eureka: Physics and Engineering
سال: 2022
ISSN: ['2461-4254', '2461-4262']
DOI: https://doi.org/10.21303/2461-4262.2022.002127