YOLO-POD: High-Precision PCB Tiny-Defect Detection Algorithm Based on Multi-Dimensional Attention Mechanism

With the widespread application of electronic devices, printed circuit boards (PCB) hold significant importance in the electronics manufacturing industry. However, due to imperfections in the manufacturing process and interference from environmental factors, tiny defects may in PCB. Therefore, the development of efficient and accurate defect detection algorithms is crucial in ensuring product quality. To address the challenge of detecting tiny defects on PCB, this paper proposes a high-precision PCB tiny defect detection algorithm based on multi-dimensional attention mechanism. To reduce model parameters and computational complexity, partial convolution (PConv) is introduced, and the ELAN module is redesigned as the more efficient P-ELAN. Additionally, to enhance the network’s feature extraction capability for tiny defects, the omni-dimensional dynamic convolution (ODConv) based on the multi-dimensional attention mechanism (MDAM) is introduced. By combining partial convolution, the POD-CSP (Partial ODConv-Cross Stage Partial) and POD-MP (Partial ODConv-Max Pooling) cross-stage partial network modules are designed, along with the OD-Neck structure. Finally, based on YOLOv7, a more efficient YOLO-POD model for small object detection is proposed, and the network is optimized during the training phase using a novel loss function called Alpha-SIoU. Experimental results demonstrate that YOLO-POD achieves a detection precision of 98.31% and recall rate of 97.09%, exhibiting substantial advantages across multiple metrics. Notably, it achieves a 28% improvement over the original YOLOv7 model, as to more stringent mAP75 metric. These results validate the high accuracy and robustness of YOLO-POD in PCB defect detection, fulfilling the requirements for high-precision detection and providing an effective detection solution for the PCB manufacturing industry. © 2024 Chinese Institute of Electronics. All rights reserved.