.png){kind=icon}
返回
A lightweight weld defect recognition algorithm based on convolutional neural networks
.png){kind=icon}
PRE
AI
DOI：10.1007/s10044-024-01315-7.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This paper proposes a lightweight weld defect-recognition algorithm based on a convolutional neural network that is appropriate for weld defect recognition in industrial welding. Specifically, the developed scheme relies on the original SqueezeNet model. However, we improve the fire module to reduce the model's parameter cardinality, introduce the ECA module to strengthen the learning of feature channels and improve the feature extraction ability of the overall model. The experimental results highlight that our algorithm's average recognition rate on the overall defects of welding depressions, welding holes, and welding burrs reaches 97.50%. Note that although our model requires substantially fewer parameters, its recognition effect is significantly improved. Our algorithm's feasibility is verified on the test data and challenged against current weld defect identification algorithms, demonstrating its enhanced identification role and application prospect.
Keyword:
Weld defect recognition
Convolutional neural networks
SqueezeNet model
ECA module
期刊
IF:
2
论文数:
1.9K
被引数:
1.9K
机构
暂无机构信息
引用论文
Automatic Defect Detection of Fasteners on the Catenary Support Device Using Deep Convolutional Neural Network基于深度卷积神经网络的接触网支撑装置紧固件缺陷自动检测
OAAI
OAAI分享收藏Investigation of Ti mesh-supported anodes for direct borohydride fuel cells直接硼氢化物燃料电池用钛网支撑阳极的研究
PREAI
分享收藏
The utilization of a GR4J model and wavelet-based artificial neural network for rainfall–runoff modelling.png)
Water SupplyOAAI
Water SupplyIF0
OAAI分享收藏Developing Wind and/or Solar Powered Crop Irrigation Systems for the Great Plains为大平原开发风能和/或太阳能作物灌溉系统
PREAI
分享收藏
查看更多内容