WLSD-YOLO: A Model for Detecting Surface Defects in Wood Lumber

As an important raw material supporting the development of society, wood lumber is widely used in the construction and furniture industries. However, traditional methods for detecting surface defects in wood face the challenges such as poor recognition, low efficiency and narrow applicability. To tackle these challenges, this paper proposes a Wood Lumber Surface-defect Detection-YOLO (WLSD-YOLO) model for the detection of surface defects in wood lumber. Firstly, this model introduces the squeeze-and-excitation (SE) attention mechanism in the backbone feature network component, which enhances the ability of capturing defects. Furthermore, a new GVC-neck layer structure is proposed to reduce the number of parameters and improve the accuracy of detection. Lastly, the combination of Normalized Weighted Distance Loss (NWD) small target detection algorithm and the Wise Intersection Over Union (WIOU) loss function is used to replace the original loss function to enhance the small target detection capability. The experimental results show that WLSD-YOLO achieves an average recognition accuracy of 76.5% for wood lumber defects. Compared with the original model YOLOv8, the mean average precision(mAP) is improved by 2.9% and the frames per second (FPS) is improved by 3.8. Meanwhile, WLSD-YOLO reduces the number of parameters to better detect several specific defects that are difficult to identify, which provides high application value for wood lumber processing and manufacturing industry.