Surface Defect Detection Model for Aero-Engine Components Based on Improved YOLOv5

Aiming at the problems of low efficiency and poor accuracy in conventional surface defect detection methods for aero-engine components, a surface defect detection model based on an improved YOLOv5 object detection algorithm is proposed in this paper. First, a k-means clustering algorithm was used to recalculate the parameters of the preset anchors to make them match the samples better. Then, an ECA-Net attention mechanism was added at the end of the backbone network to make the model pay more attention to feature extraction from defect areas. Finally, the PANet structure of the neck network was improved through its replacement with BiFPN modules to fully integrate the features of all scales. The results showed that the mAP of the YOLOv5s-KEB model was 98.3%, which was 1.0% higher than the original YOLOv5s model, and the average inference time for a single image was 2.6 ms, which was 10.3% lower than the original model. Moreover, compared with the Faster R-CNN, YOLOv3, YOLOv4 and YOLOv4-tiny object detection algorithms, the YOLOv5s-KEB model has the highest accuracy and the smallest size, which make it very efficient and convenient for practical applications.

Automated summary

This paper proposes a surface defect detection model based on an improved YOLOv5 object detection algorithm to address the low efficiency and poor accuracy issues in conventional methods. The model recalculates the parameters of preset anchors using a k-means clustering algorithm, adds an attention mechanism, and improves the neck network structure. Experimental results show that the proposed model achieves higher accuracy and lower inference time compared to other object detection algorithms, making it efficient and suitable for practical applications.