A small sample nonstandard gear surface defect detection method

To address the issues of low detection precision, slow detection speed, and imbalanced defect samples in the surface defect detection of nonstandard parts, we propose a method based on the defect detection model YOLOv8-2d and the balancing generative adversarial network (BAGAN). First, we use an attention mechanism without parameters to help the detection model focus on the defect area in the middle-level features based on the feature scale and distribution characteristics of surface defects on nonstandard parts. Second, we introduce deformable convolution and propose a path aggregation feature pyramid network (PADSFPN) with depth-separable convolution, effectively integrating multiscale feature information. Then, we use a Mish network with WIoU as the network head and extract the position and category information of the defect separately, learn through different network branches, and merge the information. Finally, we use BAGAN to expand the small sample defect dataset on the surface of nonstandard parts to achieve dataset balance. The proposed improvements are evaluated through comparative experiments in published twenty test groups, and the effectiveness of the proposed method is demonstrated. Compared with the original model, our method achieves a defect detection precision of 99.83%, a 7.7% improvement; an inference speed of 455 FPS, a 374% increase; and a mAP50 of 97.3%, a gain of 5.03%. Our proposed method maintains high precision while reducing the model's computational complexity and parameter volume, improving its efficiency and robustness in surface defect detection on nonstandard parts.