Dual-Branch Cross-Diversion Transformer With Spatial Soft Alignment for Few-Shot Surface Defect Detection

Abstract

High-performance surface defect detection is essential for industrial quality inspection, requiring accurate detection and characterisation of surface defects. While deep learning-based methods have advanced this field, challenges persist due to limited sample availability and variation in defect types. To address these challenges, we propose a new detection framework, namely, the Dual-branch Cross-Diversion Transformer with Spatial Soft Alignment, specifically designed for surface defect detection under data-scarce conditions. First, the dual-branch cross-transformer is leveraged to address data scarcity and enhance defect detection sensitivity through a few-shot pipeline. Furthermore, the Adaptive Activation Downsampling module is proposed to capture coarse-grained structural features while preserving fine-grained defect details, ensuring comprehensive surface defect characterisation. Additionally, a Cross-Diversion Self-Attention mechanism further improves multi-scale feature extraction, critical for accurate detection of diverse defect types. Finally, a Spatial Soft Alignment strategy corrects spatial misalignment between detection proposals and defect categories, reducing detection uncertainty. Through extensive experiments on two benchmark industrial datasets, our proposed architecture achieves superior performance compared to state-of-the-art methods, demonstrating its robustness and accuracy. These results demonstrate the effectiveness of the proposed method and its potential to advance surface defect detection techniques.