YOLOv4-MN3 for PCB Surface Defect Detection

Featured Application An improved YOLOv4 algorithm for PCB surface defect detection can achieve higher detection accuracy and faster detection speed with lower memory consumption and fewer multiply-accumulate operations compared with the cutting-edge YOLOv4. Surface defect detection for printed circuit board (PCB) is indispensable for managing PCB production quality. However, automatic detection of PCB surface defects is still a challenging task because, even within the same category of surface defect, defects present great differences in morphology and pattern. Although many computer vision-based detectors have been established to handle these problems, current detectors struggle to achieve high detection accuracy, fast detection speed and low memory consumption simultaneously. To address those issues, we propose a cost-effective deep learning (DL)-based detector based on the cutting-edge YOLOv4 to detect PCB surface defect quickly and efficiently. The YOLOv4 is improved upon with respect to its backbone network and the activation function in its neck/prediction network. The improved YOLOv4 is evaluated with a customized dataset, collected from a PCB factory. The experimental results show that the improved detector achieved a high performance, scoring 98.64% on mean average precision (mAP) at 56.98 frames per second (FPS), outperforming the other compared SOTA detectors. Furthermore, the improved YOLOv4 reduced the parameter space of YOLOv4 from 63.96 M to 39.59 M and the number of multiply-accumulate operations (Madds) from 59.75 G to 26.15 G.

Automated summary

The improved YOLOv4 algorithm for PCB surface defect detection achieves higher accuracy and faster speed with lower memory consumption and fewer multiply-accumulate operations compared to the cutting-edge YOLOv4. The detector performed well in experiments using a customized dataset, outperforming other state-of-the-art detectors.