Weld Image Detection and Recognition Based on Improved YOLOv4

To address the problem of low detection accuracy and recall rate in YOLOv4 of weld X- ray flaw detection defect maps, the YOLOv4-cs algorithm is designed. The algorithm improves the convolution mode of YOLOv4 and greatly reduces the model training parameters; further, it improves the accuracy of model detection by removing the down-sampling layer and fusing the feature map obtained by the second residual block in the 52x52 feature layer. Simultaneously, K- means is used to recluster the dataset and modify the priori frame of YOLOv4 model. The experimental results show that the recall rate of YOLOv4-cs in identifying three kinds of X- ray defects within aluminum alloy welded joints significantly improved, its mean average precision (mAP) was 88. 52%, which was 2. 67 percentage points higher than the original YOLOv4 model, and the detection speed increased from 20. 43 frame/s to 24. 47 frame/s.