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使用兩種不同的YOLO模型檢測印刷電路板中的缺陷

為了解決Assignee的問題，作者朴莉洋卡 這樣論述：

Nowadays, deep learning is developing in various fields. One of the most challenging tasks is the accurate detection of defects in printed circuit boards (PCBs), which has attracted more and more attention due to its impeccable performance and affordable accuracy. Deep learning is per-formed on eve

ry platform, and the results are impressive. On the other hand, the ability and accuracy of deep learning are compared to humans to some extent. We applied the concept of deep learning, which was inspired by an algorithm called You Only Look Once (YOLO). The main purpose of this research is to imple

ment the YOLO mode through convolutional neural networks, which ultimately improves accuracy and reduces error rates compared with humans. We trained an object detection model to detect defects in printed circuit boards (PCBs). In this experiment, we used 5000 images. As a result, our overall accura

cy in detecting PCB defects was 94.34%, and the batch size was 32.