## 5 Conclusions  
To address the challenges of multi-scale defect co-occurrence and weak texture defect recognition in particleboard surface detection, a lightweight detection algorithm named PBD-YOLO was proposed. In this study, a high-resolution particleboard surface defect dataset was constructed, encompassing seven types of defects, particularly including edge breakage and scratches, which exhibited weak features. Additionally, an asymmetric data augmentation strategy was employed to effectively address the issue of imbalanced sample  
Figure 14: Comparison of convergence speed during training.  
distribution within the dataset. Experimental results demonstrated that the proposed SPDDEConv down-sampling module, through its spatial partitioning-channel stacking mechanism and difference convolution branches, improved the mAP for defects with weak features but high edge sensitivity by up to 20.9%. The ShareSepHead detection head, by sharing weights to enable cross-layer knowledge transfer, enhanced the detection performance for defects with significant scale variations by 10.6% to 20.8%. Meanwhile, the C2f_SAC module further strengthened the model's ability to capture multi-scale defect features through its SAC modules. The final PBD-YOLO algorithm achieved a 5.5% improvement in mAP\({}_{50}\) and a 13% increase in recall compared to the baseline algorithm, while maintaining a lightweight parameter of 14.1 M. It outperformed mainstream detection algorithms, such as Faster R-CNN, RT-DETR, and RTMDet, in terms of accuracy-efficiency balance. This study provided an efficient and accurate automated quality inspection system for particleboard surface defect detection, offering significant practical value for advancing intelligent manufacturing upgrades and quality control.  
In the future research, this work will focus on the following directions: (i) developing particleboard surface defect generation techniques based on GANs and diffusion models [40] to address the scarcity of samples for certain defect types, and (ii) further optimizing the network architecture to design a more efficient and lightweight detection algorithm, aiming to enhance detection speed while balancing the requirements of high-speed production lines and maintaining stable recall rates.  
Conceptualization, H.G., Z.C. and J.Y.; methodology, H.G., Z.C. and L.Y.; software, H.G. and H.D.; validation, H.G.; formal analysis, H.G., H.D. and P.C.; investigation, H.G.; resources, J.Y., L.Y. and P.C.; data curation, Z.C. and H.D.; writing--original draft preparation, H.G. and Z.C.; writing--review and editing, L.Y., P.C. and J.Y.; visualization, H.G.; supervision, L.Y. and J.Y.; project administration, P.C.; funding acquisition, J.Y. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Key R&D Program of China (Grant No. 2023 YFD2201500).  
Not applicable.  
The data will be made available upon request from the authors once all studies have been completed and published.  
The authors gratefully acknowledge Tangxian Huiyin Wood Industry Co., Ltd. for their essential technical collaboration and on-site support during the acquisition of defect image datasets.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
YOLO & You Only Look Once  
PBD-YOLO & Particleboard Defect-You Only Look Once  
SPDDEConv & Space to Depth and Difference Enhance Convolution  
ShareSepHead & Share Separated Head  
SAC & Switchable Atrous Convolution  
C2f_SAC & C2f module with Switchable Atrous Convolution  
CBAM & Convolutional block attention module