LSYOLO: an algorithm for linear scan PCB defect detection

Abstract Aiming at the current problems that different defects in linear scan PCB have scale differences and some defects have high similarity with the background, which are difficult to localize and classify, an expanded receptive field PCB defect detection algorithm is proposed to be applied to the defect detection of linear scan circuit boards. The expanded receptive field module (ERFM) is used in the backbone of YOLOv8 to replace C2f, which can avoid information loss and gridding artifacts while obtaining better contextual information to improve the detection performance of defects with high background similarity. Then, the spatial selective feature pyramid (SSFPN) is used as the FPN to enhance the model’s ability to detect defects at different scales while reducing the model performance requirements by utilizing the information of the spatial dimensions of the feature maps to fuse the feature maps at different scales. Wise-intersection over union is used as the bounding box loss function, and slide loss is used as the classification loss function to enhance the model’s focus on difficult-to-localize and difficult-to-classify samples. Comparison experiments are conducted on a linear scan printed circuit board dataset, and the experimental results show that the improved model obtains a significant improvement in the detection performance of defects with high detection difficulty; the average precision of the overall defects is improved by 9.6%, the number of model parameters is reduced by 40%, the amount of computation is reduced by 20%, and the size of the model weights file is only 3.64MB. Detecting defects in linear scan PCB is more efficient and lighter than other algorithms.