PCES-YOLO: High-Precision PCB Detection via Pre-Convolution Receptive Field Enhancement and Geometry-Perception Feature Fusion

Printed circuit board (PCB) defect detection faces challenges like small target feature loss and severe background interference. To address these issues, this paper proposes PCES-YOLO, an enhanced YOLOv11-based model. First, a developed Pre-convolution Receptive Field Enhancement (PRFE) module replaces C3k in the C3k2 module. The ConvNeXtBlock with inverted bottleneck is introduced in the P4 layer, greatly improving small-target feature capture and semantic understanding. The second key innovation lies in the creation of the Efficient Feature Fusion and Aggregation Network (EFAN), which integrates a lightweight Spatial-Channel Decoupled Downsampling (SCDown) module and three innovative fusion pathways. This achieves substantial parameter reduction while effectively integrating shallow detail features with deep semantic features, preserving critical defect information across different feature levels. Finally, the Shape-IoU loss function is incorporated, focusing on bounding box shape and scale for more accurate regression and enhanced defect localization precision. Experiments on the enhanced Peking University PCB defect dataset show that PCES-YOLO achieves a mAP50 of 97.3% and a mAP50–95 of 77.2%. Compared to YOLOv11n, it shows improvements of 3.6% in mAP50 and 15.2% in mAP50–95. When compared to YOLOv11s, it increases mAP50 by 1.0% and mAP50–95 by 5.6% while also significantly reducing the model parameters. The performance of PCES-YOLO is also evaluated against mainstream object detection algorithms, including Faster R-CNN, SSD, YOLOv8n, etc. These results indicate that PCES-YOLO outperforms these algorithms in terms of detection accuracy and efficiency, making it a promising high-precision and efficient solution for PCB defect detection in industrial settings.