Wood Panel Defect Detection Based on Improved YOLOv8n

Abstract

Wood panel surface defect detection is critical to product quality. Traditional detection methods are time-consuming and subjective, and they can lead to economic waste, while deep learning image recognition techniques offer a new approach. However, the accuracy and convergence speed of existing defect detection techniques still require improvement. In this paper, an improved algorithm based on YOLOv8n was designed for accurate detection of wood panel defects. The C-ADown method was designed to replace traditional downsampling, while preserving high-frequency features. The combination of the Dilation-wise Residual Module and multi-scale dilation attention was employed to enhance the multiscale robustness of defect detection. A hybrid encoder was added to improve localization accuracy. The loss function was optimized to improve detection accuracy and convergence speed. Compared to the base YOLOv8 version, the improved model achieved a 6.1% increase in mAP, an 8% increase in recall, and a 3.6% increase in precision, significantly enhancing the model’s detection capabilities. The GitHub link to the improved algorithm files is as follows: (https://github.com/humblefactos1/YOLOV8-CDC/tree/main.)