Laser & Optoelectronics Progress, Volume. 59, Issue 16, 1610002(2022)
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 52×52 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.
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Song Cheng, Jintao Dai, Honggang Yang, Yunxia Chen. Weld Image Detection and Recognition Based on Improved YOLOv4[J]. Laser & Optoelectronics Progress, 2022, 59(16): 1610002
Category: Image Processing
Received: May. 6, 2021
Accepted: Jul. 5, 2021
Published Online: Jul. 22, 2022
The Author Email: Chen Yunxia (chenyx@sdju.edu.cn)