关键词: YOLOv11, 轮毂缺陷检测, 透视校正, 轻量化结构, ECA注意力机制

Abstract: In industrial visual inspection of wheel hub surface defects, geometric distortion caused by viewpoint deviation together with the small scale and complex morphology of defect instances has limited the performance of existing detection methods. To address these challenges, a defect-detection approach that combines perspective rectification with a lightweight attention mechanism was proposed. Firstly, an ellipse was fitted to the rim contour, and its offset from the geometric center was employed to construct a perspective quadrilateral; the corresponding homography matrix was then solved to rectify the image, thereby mitigating distortion during subsequent feature extraction. Secondly, the conventional CBS convolutional blocks in the Backbone and Neck of YOLOv11 were replaced by lightweight Ghost convolution modules to reduce the number of parameters and the overall computational cost. Meanwhile, an Efficient Channel Attention (ECA) mechanism was introduced to enhance the network's sensitivity to tiny defect regions, leading to the construction of the improved model YOLOv11n-GAConv. Experiments on a self-built wheel hub defect dataset showed that the proposed model achieved a mean Average Precision (mAP@0.5) of 84.7 %, with improvements of 2.4 percentage points in mAP@0.5 and 8.6 percentage points in recall compared to YOLOv11n. The number of parameters and computational cost were reduced by 12.4% and 11.1%, respectively, effectively reducing the model complexity while achieving improved detection accuracy.

Key words: YOLOv11, wheel hub defect detection, perspective correction, lightweight structure, ECA mechanism