An Efficient Tiny Defect Detection Method for PCB With Improved YOLO Through a Compression Training Strategy

Tiny defect detection is a knotty task in industrial electronics production. Existing traditional and deep learning methods have achieved satisfactory performance, however, they still face challenges in accuracy, generalization ability, and computational complexity. Therefore, this study designs a Tiny Defect Detection-based You Only Look Once (TDD-YOLO) model and proposes an innovative compression training strategy to train on low-resolution images and test on original images. Firstly, a four-ME layers structure is adopted to the backbone network, to integrate more underlying information and extract effective features. In addition, a miniature detection head is incorporated into the head network to improve the accuracy and generalization performance of YOLO. Meanwhile, TDD-YOLO introduces Wise Intersection over Union (W-IoU) to re-evaluate the loss of bounding box regression and reduce false negatives by fitting the model well to regular quality anchor boxes. Finally, an image compression method at different ratios is applied in the proposed compression training strategy, to reduce computational complexity and surprisingly further improve accuracy. Comprehensive experiments on several variable compressed datasets which are based on a public Printed Circuit Board (PCB) defect dataset validate the effectiveness of our theoretical approach and illustrate that our proposed method outperforms state-of-the-art methods.