DMR-YOLO: A Lightweight Visual Inspection Method for Surface Defect Detection of Aero-Engine Components

Accurate surface defect detection is essential for ensuring the measurement accuracy and assembly reliability of aero-engine components during manufacturing and assembly processes. Bearings, as critical rotating components in aero-engines, are highly sensitive to surface defects that may lead to stress concentration and premature failure. However, complex defect types, low-contrast textures, and multi-scale characteristics pose significant challenges for existing lightweight visual inspection models. To address these issues, this paper proposes an improved lightweight detection model, termed DMR-YOLO, based on YOLOv8n. A Diverse Branch Block (DBB) is introduced to enhance multi-scale feature extraction and improve the representation of complex defect patterns. A Multi-Level Channel Attention (MLCA) mechanism is embedded to strengthen discriminative feature channels and suppress background interference caused by low-contrast textures. In addition, a ResidualADown module is designed to preserve critical feature information during downsampling, improving the detection of subtle defects. Experimental results on a bearing surface defect dataset show that the proposed model achieves an mAP of 89.3%, representing a 2.8% improvement over YOLOv8n while maintaining real-time inference at 138.6 FPS. Moreover, generalization tests conducted on a steel surface defect dataset demonstrate the robustness and transferability of the proposed method across different datasets.