In wheel hub surface defect detection tasks in industrial visual inspection， geometric distortion caused by shooting angle deviation as well as the small scale and complex morphology of defect objects limit performance of the existing detection methods. To address these challenges， a defect detection method that combines perspective correction and lightweight attention mechanism was proposed. Firstly， the offset relationship between the ellipse center and the geometric center of the wheel hub was utilized to construct a perspective transformation quadrilateral for solving the homography matrix， so as to complete image perspective correction， thereby mitigating the impact of distortion on subsequent feature extraction. Secondly， based on YOLOv11 model， the conventional CBS （Convolution-BatchNorm-SiLU（Sigmoid Linear Unit）） modules in the backbone and neck were replaced by lightweight Ghost convolutions to reduce the number of parameters and the computational cost of the model. Meanwhile， an Efficient Channel Attention （ECA） mechanism was introduced to enhance the network’s perceptual ability to tiny defect regions， leading to the construction of the improved model YOLOv11n-GAConv. Experimental results on a self-built wheel hub defect dataset show that the mean Average Precision of the proposed model when the intersection over union threshold between the predicted bounding boxes and the ground truth bounding boxes is set to 0.5 （mAP@0.5） of 84.7%， with an improvement of 2.4 percentage points compared to that of YOLOv11n， and achieves a recall of 79.5%， with an improvement is 8.6 percentage points compared to that of YOLOv11n. At the same time， the number of parameters and computational cost of the proposed model are reduced by 12.4% and 11.1%， respectively， compared to those of YOLOv11n. It can be seen that the proposed method reduces the model complexity while achieving improved detection precision.