CMNet: Hybrid CNN–Mamba Network for Fabric Defect Detection

Fabric defect detection plays a vital role in the quality control of the textile manufacturing industry. However, it remains challenging because of defect diversity, complexity, and environmental factors. Deep learning-based methods efficiently extract visual features, improving detection accuracy and inference speed. However, most deep learning methods fail to adequately capture tiny, irregular, and blurred-edge defect features due to diverse fabric texture backgrounds and complex defect traits. To address these issues, we propose CMNet, a novel hybrid CNN–Mamba detection network with a dual-branch, heterogeneous feature-extraction architecture. First, we propose an Adaptive Weighted Adjacent Context Coordination Module (AWA-CCM) to enhance features of tiny fabric defects and edge textures while effectively suppressing interference from complex backgrounds. Second, we propose a Multi-Scale Large-Kernel Attention Mamba (MLAMamba) module to improve the network’s global representation capability for defects with variable scales and irregular shapes. Finally, we construct a Bidirectional Fusion Module (BFM) to dynamically balance local detail information and global structural information for efficient, complementary feature fusion. Experimental results on our self-developed fabric datasets, obtained using six widely adopted metrics, show that CMNet significantly outperforms state-of-the-art methods.