Unsupervised Anomaly Detection in Printed Circuit Boards through Student–Teacher Feature Pyramid Matching
نویسندگان
چکیده
Deep learning methods are currently used in industries to improve the efficiency and quality of product. Detecting defects on printed circuit boards (PCBs) is a challenging task usually solved by automated visual inspection, optical manual supervised methods, such as you only look once (YOLO) tiny YOLO, YOLOv2, YOLOv3, YOLOv4, YOLOv5. Previously described for defect detection PCBs require large numbers labeled images, which computationally expensive training requires great deal human effort label data. This paper introduces new unsupervised method PCB using student–teacher feature pyramid matching pre-trained image classification model learn distribution images without anomalies. Hence, we extracted knowledge into student network had same architecture teacher network. one-step transfer retains key clues much possible. In addition, incorporated multi-scale strategy framework. A mixture multi-level from features passes through better supervision, known hierarchical alignment, allows receive it, thereby allowing various sizes scoring function reflects probability occurrence framework helped us achieve accurate anomaly detection. Apart accuracy, its inference speed also reached around 100 frames per second.
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ژورنال
عنوان ژورنال: Electronics
سال: 2021
ISSN: ['2079-9292']
DOI: https://doi.org/10.3390/electronics10243177