Metal-YOLO Detection Algorithm for Defects in Coaxial Packaged Metal Base
To resolve issues of insufficient detection accuracy,false detection,and missing detection in defect detection of coaxial packaged metal bases,this paper proposes an improved model called Metal-YOLO,which builds upon YOLO v5s.By introducing cross-layer feature enhancement connection(CFEC),the model ability to represent complex small object defects is substantially enhanced,effectively reducing the missing detection rate.To further improve the model ability to perceive and discriminate defect features across different scales,an adaptive attention module is integrated into the model,which effectively minimizes background information.Additionally,recognizing the shortcomings of the complete intersection over union(CIoU)loss function in the localization of defect object boxes,the effective intersection over union(EIoU)loss function is adopted.This change remarkably improves the precision of the prediction box positioning.Experimental results demonstrate that Metal-YOLO excels in metal surface defect detection tasks.Furthermore,the proposed model achieves a recall rate and mean average precision values of 74.1%and 78.3%,showing an improvement of 5.0 percentage points and 4.1 percentage points,respectively,compared to the baseline model YOLO v5s,substantially enhancing the effectiveness of metal surface defect detection.
machine visiondefect detectionYOLO algorithmcoaxial packaged metal base
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