To solve the false detection and missed detection problems caused by various types and sizes of defects in the detection of steel surface defects, similar defects and background features, and similarities between different defects, this paper proposes a lightweight detection model named multiscale edge and squeeze-and-excitation attention detection network (MSESE), which is built upon the You Only Look Once version 11 nano (YOLOv11n). To address the difficulty of locating defect edges, we first propose an edge enhancement module (EEM), apply it to the process of multiscale feature extraction, and then propose a multiscale edge enhancement module (MSEEM). By obtaining defect features from different scales and enhancing their edge contours, the module uses the dual-domain selection mechanism to effectively focus on the important areas in the image to ensure that the feature images have richer information and clearer contour features. By fusing the squeeze-and-excitation attention mechanism with the EEM, we obtain a lighter module that can enhance the representation of edge features, which is named the edge enhancement module with squeeze-and-excitation attention (EEMSE). This module was subsequently integrated into the detection head. The enhanced detection head achieves improved edge feature enhancement with reduced computational overhead, while effectively adjusting channel-wise importance and further refining feature representation. Experiments on the NEU-DET dataset show that, compared with the original YOLOv11n, the improved model achieves improvements of 4.1% and 2.2% in terms of mAP@0.5 and mAP@0.5:0.95, respectively, and the GFLOPs value decreases from the original value of 6.4 to 6.2. Furthermore, when compared to current mainstream models, Mamba-YOLOT and RTDETR-R34, our method achieves superior performance with 6.5% and 8.9% higher mAP@0.5, respectively, while maintaining a more compact parameter footprint. These results collectively validate the effectiveness and efficiency of our proposed approach.

Printed circuit boards (PCBs) provide stable connections between electronic components. However, defective printed circuit boards may cause the entire equipment system to malfunction, resulting in incalculable losses. Therefore, it is crucial to detect defective printed circuit boards during the generation process. Traditional detection methods have low accuracy in detecting subtle defects in complex background environments. In order to improve the detection accuracy of surface defects on industrial printed circuit boards, this paper proposes a residual large kernel network based on YOLOv5 (You Only Look Once version 5) for PCBs surface defect detection, called YOLO-RLC (You Only Look Once-Residual Large Kernel). Build a deep large kernel backbone to expand the effective field of view, capture global information more efficiently, and use 1 × 1 convolutions to balance the depth of the model, improving feature extraction efficiency through reparameterization methods. The neck network introduces a bidirectional weighted feature fusion network, combined with a brand-new noise filter and feature enhancement extractor, to eliminate noise information generated by information fusion and recalibrate information from different channels to improve the quality of deep features. Simplify the aspect ratio of the bounding box to alleviate the issue of specificity values. After training and testing on the PCB defect dataset, our method achieved an average accuracy of 97.3% (mAP50) after multiple experiments, which is 4.1% higher than YOLOv5-S, with an average accuracy of 97.6% and an Frames Per Second of 76.7. The comparative analysis also proves the superior performance and feasibility of YOLO-RLC in PCB defect detection.