Surface Defect Detector Based on Deformable Convolution and Lightweight Multi-Scale Attention

Abstract

The detection of defects on industrial surfaces is essential for guaranteeing the quality and safety of products. Deep learning-based object detection methods have demonstrated impressive efficacy in industrial applications in recent years. However, due to the complex and variable shape of defects, the similarity between defects and background, large intra-class differences, and small inter-class differences lead to low classification accuracy, it is a great challenge to achieve accurate defect detection. To overcome these challenges, this research proposed a novel network specifically designed for defect detection. First, a feature extraction network, ResDCA-Net, is constructed based on deformable convolution and lightweight multi-scale attention, where deformable convolution can adaptively adjust to extract features of defects with complex and variable shapes. Second, the lightweight multi-scale attention module is constructed, which uses multi-branch and cross-space fusion to obtain the complete feature space attention map, thereby improving the defect feature attention and reducing the background feature attention. Third, to enhance the classification and localization accuracy, an attention-based decoupled prediction module is proposed to ensure that the classification and regression branches of the model can focus on their required features. Finally, extensive comparative experiments indicate that the proposed approach performs best, achieving 83.7% and 83.4% mean Average Precision (mAP) on the GC10-DET and NEU-DET datasets, respectively. The effectiveness of the proposed individual modules is further validated in ablation experiments, which demonstrate the excellent performance and potential in defect detection tasks.