SFD-YOLO: A Multi-Angle Scattered Field-Based Optical Surface Defect Recognition Method

The surface quality of optical components plays a decisive role in advanced imaging, precision manufacturing, and high-power laser systems, where even defects can induce abnormal scattering and degrade system performance. Addressing the limitations of conventional single-view inspection methods, this study presents a panoramic multi-angle scattered light field acquisition approach integrated with deep learning-based recognition. A hemispherical synchronous imaging system is designed to capture complete scattered distributions from surface defects in a single exposure, ensuring both structural consistency and angular completeness of the measured data. To enhance the interpretation of complex scattering patterns, we develop a tailored lightweight network, SFD-YOLO, which incorporates the PSimam attention module for improved salient feature extraction and the Efficient_Mamba_CSP module for robust global semantic modeling. Using a simulated dataset of multi-width scratch defects, the proposed method achieves high classification accuracy with strong generalization and computational efficiency. Compared to the baseline YOLOv11-cls, SFD-YOLO improves Top-1 accuracy from 92.5% to 95.6%, while reducing the parameter count from 1.54 M to 1.25 M and maintaining low computational cost (Flops 4.0G). These results confirm that panoramic multi-angle scattered imaging, coupled with advanced neural architectures, provides a powerful and practical framework for optical surface defect detection, offering valuable prospects for high-precision quality evaluation and intelligent defect inversion in optical inspection.