Detection of Welding Defects Tracked by YOLOv4 Algorithm

被引：0
作者：
Chen, Yunxia [1 ]
Wu, Yan [1 ]
机构：
[1] Shanghai Polytech Univ, Sch Intelligent Mfg & Control Engn, Shanghai 201209, Peoples R China
来源：
APPLIED SCIENCES-BASEL | 2025年 / 15卷 / 04期
基金：
中国国家自然科学基金;
关键词：
weld defects; deep learning; target detection; YOLOv4;
D O I：
10.3390/app15042026
中图分类号：
O6 [化学];
学科分类号：
0703 ;
摘要：
The recall rate of the original YOLOv4 model for detecting internal defects in aluminum alloy welds is relatively low. To address this issue, this paper introduces an enhanced model, YOLOv4-cs1. The improvements include optimizing the stacking method of residual blocks, modifying the activation functions for different convolutional layers, and eliminating the downsampling layer in the PANet (Pyramid Attention Network) to preserve edge information. Building on these enhancements, the YOLOv4-cs2 model further incorporates an improved Spatial Pyramid Pooling (SPP) module after the third and fourth residual blocks. The experimental results demonstrate that the recall rates for pore and slag inclusion detection using the YOLOv4-cs1 and YOLOv4-cs2 models increased by 28.9% and 16.6%, and 45% and 25.2%, respectively, compared to the original YOLOv4 model. Additionally, the mAP values for the two models are 85.79% and 87.5%, representing increases of 0.98% and 2.69%, respectively, over the original YOLOv4 model.
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页数：15
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