PBD-YOLO: Dual-Strategy Integration of Multi-Scale Feature Fusion and Weak Texture Enhancement for Lightweight Particleboard Surface Defect Detection

Surface defect detection plays an important role in particleboard quality control. But it still faces challenges in detecting coexisting multi-scale defects and weak texture defects. To address these issues, this study proposed PBD-YOLO (Particleboard Defect-You Only Look Once), a lightweight YOLO-based algorithm with multi-scale feature fusion and weak texture enhancement capabilities. In order to improve the ability of the algorithm to extract weak texture features, the SPDDEConv (Space to Depth and Difference Enhance Convolution) module was introduced in this study, which reduced the loss of information in the down-sampling process through space-to-depth transformation and enhanced the edge information of weak texture defects through difference convolution. This approach improved the mAP (mean average precision) of weakly featured but edge-sensitive defects (such as scratches) by as much as 20.9%. In order to improve the algorithm’s ability to detect multi-scale defects, this study introduced the ShareSepHead (Share Separated Head) and C2f_SAC (C2f module with Switchable Atrous Convolution) modules. ShareSepHead fused feature maps from different scales of the neck network by adding a convolutional layer with shared weights, and the C2f_SAC module adaptively fused multi-rate receptive fields through a switching mechanism. The synergistic effect of ShareSepHead and C2f_SAC improved the detection accuracy of multi-scale defects by 10.6–20.8%. The experimental results demonstrated that PBD-YOLO achieved 85.6% mAP at 50% intersection over union (IoU) and 81.4% recall, surpassing YOLOv10 by 5.5% and 13%, respectively, while reducing parameters by 11.3%. In summary, it could be better to meet the need of accurately detecting surface defects on particleboard.