  With the rapid growth of the PCB manufacturing industry, there is an
increasing demand for computer vision inspection to detect defects during
production. Improving the accuracy and generalization of PCB defect detection
models remains a significant challenge. This paper proposes a high-precision,
robust, and real-time end-to-end method for PCB defect detection based on deep
Convolutional Neural Networks (CNN). Traditional methods often suffer from low
accuracy and limited applicability. We propose a novel approach combining
YOLOv5 and multiscale modules for hierarchical residual-like connections. In
PCB defect detection, noise can confuse the background and small targets. The
YOLOv5 model provides a strong foundation with its real-time processing and
accurate object detection capabilities. The multi-scale module extends
traditional approaches by incorporating hierarchical residual-like connections
within a single block, enabling multiscale feature extraction. This
plug-and-play module significantly enhances performance by extracting features
at multiple scales and levels, which are useful for identifying defects of
varying sizes and complexities. Our multi-scale architecture integrates feature
extraction, defect localization, and classification into a unified network.
Experiments on a large-scale PCB dataset demonstrate significant improvements
in precision, recall, and F1-score compared to existing methods. This work
advances computer vision inspection for PCB defect detection, providing a
reliable solution for high-precision, robust, real-time, and domain-adaptive
defect detection in the PCB manufacturing industry.
