SGM-YOLO: YOLO-Based Defect Detection Model for Wood Lumber

Abstract

Wood lumber is widely used in the construction and furniture manufacturing industries. In order to solve the problems of poor recognition, low efficiency and few detection types of manual and traditional processing methods, this paper proposes a model SGM-YOLO for the detection of surface defects in wood lumber. The SGM-YOLO model references a new backbone feature network, SL-backbone, to enhance the model’s ability to detect defects of different sizes. And, a new GVE-neck layer structure will be proposed in this paper, which reduces the parameters as well as the accuracy. In addition, the Normalized Weighted Distance Loss (NWD) small target detection algorithm is combined with the MPDIOU boundary loss function to replace the original loss function to further enhance the small target detection capability. Experiments show that SGM-YOLO achieves an average recognition accuracy of 77.4% for wood lumber defects, compared with the original model YOLOv8, the mAP is improved by 3.8% and the FPS is improved by 4.4, while the number and size of parameters are reduced, which provides better detection of several defects that are difficult to be identified. The methods presented in this paper were also applied to the YOLOv5 model, yielding positive results, to confirm its generalizability. The results demonstrate the high application value of the SGM-YOLO model in the wood lumber processing and manufacturing industry’s final product inspection.