DC-Mamba for surface micro defect classification on large aperture optics with multi axis attention

Large-aperture optics play a pivotal role in high-performance optical systems, and the presence of micro-defects on their surfaces can significantly degrade system performance and reliability. Traditional methods for detecting these defects face challenges due to their small size, multiplicity, and complexity. This paper proposes an improved Mamba-based defect classification method, DC-Mamba, specifically designed for detecting surface micro-defects on large-aperture optics. DC-Mamba replaces the original model's scanning mechanism and neck structure with a multi-axis interactive 2D Selective Scan (MISS2D) and a Multi-axis Interactive Feature Pyramid Network (MIFPN), achieving coordinated modeling of positional information, hierarchical structures, spatial relationships, and channel-wise features in the input feature maps. Evaluation on the NEU-DET dataset demonstrates that DC-Mamba, with MACSA, increases the AP from 41.7% to 45.7% and AP50 from 72.2% to 74.7%, compared to the original VMamba model. Furthermore, DC-Mamba achieves an AP of 64.3% on our self-made optic surface micro-defect (OSMD) dataset. By effectively distinguishing micro-defects from interference points, DC-Mamba provides a robust solution for intelligent defect detection on large-aperture optics surfaces.