SFNet for Surface Weak Defect Recognition in Particleboard

Particleboard has been widely used in furniture manufacturing, architectural decoration, packaging and logistics applications, and transportation, owing to its strong raw material adaptability, relatively low cost, and favorable processing performance, making it one of the foundational materials in the wood-based panel industry. Its surface quality directly determines the added value of final products. However, in actual production environments, the complex background textures of particleboard surfaces and the low contrast between defects and the background pose substantial challenges to automatic surface defect recognition. To address these issues, this paper proposes SFNet, a particleboard surface defect recognition network integrating spatial-domain and frequency-domain feature enhancement. The network adopts EfficientNet-B0 as its backbone, introduces a Dynamic Matrixed Color Correction (DMCC)module after Block 4 to adaptively adjust feature channel weights via a dynamic temperature mechanism, thereby enhancing the feature saliency of defects in the spatial domain, and embeds a Wavelet Transform Convolution module (WTConv) after Block 6 to map spatial features into the frequency domain, leveraging the differences in frequency response between defects and the background to improve the model’s sensitivity to high-frequency defect features and multi-scale texture information. The dataset comprised 2405 high-confidence defective image patches, including 2005 patches for the main classification experiment and 400 independent patches for illumination-robustness testing. Experimental results show that SFNet was evaluated on a test set of 401-images encompassing five types of defects on particleboard surfaces, namely shavings, dust spots, oil spots, glue spots, and pollution. Across five random seeds, the model achieved an average accuracy of 97.86% ± 0.28%, with the highest single-run accuracy reaching 98.25%. It outperforms the baseline classification models used for comparison in terms of accuracy, precision, recall, and F1-score. Grad-CAM visualization results further demonstrate that SFNet can effectively suppress interference from complex background textures and accurately focus on defective regions. These results indicate that the proposed method exhibits strong robustness and recognition stability, providing an efficient and reliable solution for automated particleboard surface quality inspection in industrial scenarios.