Detection of wood grain defects based on edge prior aggregation

Wood, a widely distributed renewable resource, plays a vital role in accelerating urbanisation. However, wood grain defects pose significant safety hazards. Detecting these defects is challenging due to low image clarity and contrast, as well as similar colours between defective and non-defective regions. We propose a novel detection network, EPANet, which leverages edge priori enhancement to address these challenges. EPANet includes a global edge priori enhancement module to capture key contextual information and a local edge priori enhancement module to highlight important edge features. This dual approach improves the network’s focus on defect regions and enhances detection accuracy. On publicly available datasets, EPANet achieved an AP50 of 0.869 for single-grain defects and 0.914 for multiple-grain defects, representing at least a 16.8% improvement over baseline methods. Our algorithm outperformed existing texture defect detection algorithms, demonstrating superior robustness in handling multiple noises. EPANet significantly enhances the detection of wood grain defects, ensuring safer and more efficient wood production. The proposed edge priori aggregation modules contribute to the network’s superior performance, making it a valuable tool for real-time wood defect detection.