Printed Circuit Board Defect Detection Based on Feature Pyramid Spatial Prior Network

In modern electronic devices, printed circuit boards play a crucial role as core components, and their quality is essential for device performance and reliability. Due to the complexity of PCB manufacturing processes, issues such as open circuits, short circuits, and solder defects often occur, affecting the overall quality of products. Traditional manual inspection methods are unable to meet high standards, leading to the gradual adoption of automated defect detection technology as the mainstream trend in the industry. This paper presents a network model for PCB defect detection, aimed at addressing the challenge of detecting small targets. We propose SmallObjectEnhancePyramid, which utilizes the P2 feature layer processed by SPDConv to obtain features rich in small object information and fuses them with P3. Then, we integrate features using CSP-OmniKernel to effectively learn feature representations from global to local. Additionally, we introduce RMTBlock to improve RepC3, reducing the computational burden of global information and model parameter quantity. This study not only improves the accuracy of PCB defect detection, but also helps optimize manufacturing processes and enhance production efficiency. Experimental results show that FPSPNet effectively improves the detection accuracy of printed circuit board defects, achieving an mAP0.5 value of 98.8%, precision of 99.3%, and recall of 97.3% on the dataset. It also outperforms existing models in terms of performance.