YOLOv5-based defect recognition for aluminum profile surface

Surface defects in aluminum profiles have a significant impact on product performance, safety, and reliability. Traditional physical-based inspection methods suffer from high costs, inefficiency, and lack of visualization of the inspection process. Machine learning-based inspection methods, which rely on artificially designed features, face limitations in detection versatility and vulnerability to interference from the external environment. To address these challenges, this paper proposes an improved surface defect detection method based on YOLOv5. To enhance the model's ability to detect targets of various sizes, we incorporated the SKA module into the network's backbone, which enables the model to dynamically determine the size of the receiving field based on the input data. And we also add up-sampling and tensor stitching operations to the neck network to expand the amount of data in the feature dimension. A direct path was established between the backbone network and the PANet architecture to enhance the feature fusion capability of the model. In addition, we also introduce SAC in the vicinity of the detection head to further improve the detection performance of the model. The experimental results demonstrate that our proposed model can effectively identify various types of surface defects on aluminum profiles. Compared to the original model, the recall rate is increased by 2.0%, the mAP@0.5 is increased by 2.2% and the inference speed can reach 58.7 FPS.