An Efficient Model for Detection of Wood Surface Defects

Detection of wood surface defects is one critical step in the plywood process. YOLO series detection models need to find a good tradeoff between detection speed and accuracy. Therefore, this paper improves the YOLOv8 model by replacing the backbone, neck, and head with the FasterNet, Enhanced Feature Pyramid Network (EFPN), and Multi-Scale Feature Decoupling Head (MSDH), respectively. The FasterNet reduces the number of model parameters by using Partial Convolution (PConv). To remedy the accuracy loss caused by the FasterNet, the EFPN adds auxiliary branches to reduce information loss between different feature layers and uses Dilated Reparameter Modules (DRM) to extract more multi-scale features. Besides, the MSDH reorganizes the features on different scales to enhance their semantic relationships. Experimental results show that FasterNet reduces the number of parameters by 43%, the EFPN and the MSDH increase the mAP (mean Average Precison) by 2.2% and 1.4%, respectively, compared to the original Yolov8 model.