MWYOLO: A Mamba-Enhanced Lightweight YOLO Framework with Multi-Frequency Attention for Industrial Surface Defect Detection

Industrial surface defect detection constitutes a fundamental component in automated quality inspection but remains challenging due to complex textures, diverse defect scales, and stringent real-time constraints. To address these issues, we present MWYOLO, an enhanced YOLO11-based detection framework tailored for accurate and efficient industrial inspection. First, a C3k2-Mamba Spatial Fusion Block (C3k2-MSFB) that integrates global contextual information with local structural cues via state-space modeling, enabling more discriminative representations of fine-grained texture variations. Second, a multi-scale wavelet attention (MWA) module is embedded into the backbone, leveraging wavelet-domain feature decomposition and dual attention to capture multi-frequency patterns, thereby improving sensitivity to fine-grained and subtle defect patterns. Third, an Inner-CIoU loss is developed to emphasize interior geometric alignment during bounding-box regression, offering more stable optimization for ambiguous or low-contrast targets. Extensive experiments conducted on three representative industrial datasets—NEU-DET, HRIPCB, and a self-constructed GSD dataset—demonstrate the effectiveness of MWYOLO. The model achieves mAP50 scores of 81.4%, 98.1%, and 67.7%, respectively, while maintaining a lightweight design with only 3.2M parameters and 7.3 GFLOPs. The results validate MWYOLO as a robust and computationally efficient solution, offering a favorable balance between accuracy, interpretability, and deployability for real-world industrial defect detection tasks.