YOLO-HMC: An Improved Method for PCB Surface Defect Detection

The surface defects of printed circuit boards (PCBs) generated during the manufacturing process have an adverse effect on product quality, which further directly affects the stability and reliability of equipment performance. However, there are still great challenges in accurately recognizing tiny defects on the surface of PCB under the complex background due to its compact layout. To address the problem, a novel YOLO-HorNet-MCBAM-CARAFE (YOLO-HMC) network based on improved YOLOv5 framework is proposed in this article to identify the tiny-size PCB defect more accurately and efficiently with fewer model parameters. First, the backbone part adopts the HorNet for enhancing the feature extraction ability and deepening the information interaction. Second, an improved multiple convolutional block attention module (MCBAM) is designed to improve the ability of the model to highlight the defect location from a highly similar PCB substrate background. Third, the content-aware reassembly of features (CARAFE) is used to replace the up-sampling layer for fully aggregating the contextual semantic information of PCB images in a large receptive field. Moreover, aiming at the difference between PCB defect detection and natural detection, the original model detection head (DH) is optimized to ensure that YOLOv5 can accurately detect PCB tiny defects. Extensive experiments on PCB defect public datasets have demonstrated a significant advantage compared with several state-of-the-art models, whose mean average precision (mAP) can reach 98.6%, verifying the accuracy and applicability of the proposed YOLO-HMC.