Recognition of weld defects from X-ray images based on improved convolutional neural network

doi:10.1007/s11042-022-12546-3

	Recognition of weld defects from X-ray images based on improved convolutional neural network
	Hu, Ande 2; Wu, Lijian 3; Huang, Jiankang 3; Fan, Ding 2; Xu, Zhenya 1
刊名	Multimedia Tools and Applications
	2022-05-01
卷号	81 期号:11 页码:15085-15102
关键词	Chemical activation Convolution Convolutional neural networks Defects Image enhancement Image segmentation Processing Welds Activation functions Convolution neural network Convolutional neural network Exponential linear unit function Exponentials Linear units Pooling Unit functions Weld defects Weld defects recognition
ISSN号	1380-7501
DOI	10.1007/s11042-022-12546-3
英文摘要	When convolutional neural network (CNN) is used for welding defect detection image recognition, the recognition result will be affected by many factors such as human factors, the activation function is sensitive to input parameters, and the edge features are weakened. In order to overcome the above problems, the methods include image processing, exponential linear unit (ELU) activation function and improved pooling model are used. According to the experiment, the image processing method can effectively segment the weld and defects, and the defect location in the weld image can be located. Using the ELU activation function in the CNN model can improve the robustness of the neural network to the input parameters and increase the sparsity of the network to increase the model’s convergence speed. The improved pooling method based on grayscale adaptation can increase the extraction range of weld defect features and reduce the impact of noise, and has certain dynamic adaptability to the defect features. The result shows that the improved convolutional neural network(ICNN) method can effectively improve the accuracy of recognition in weld image recognition, and the overall recognition rate can reach 98.13%. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
语种	英语
出版者	Springer
内容类型	期刊论文
源URL	[http://ir.lut.edu.cn/handle/2XXMBERH/158400]
专题	材料科学与工程学院
作者单位	1.Baoshan Iron and Steel Co., Ltd, Shanghai; 201900, China 2.State Key Laboratory of Advanced Processing and Recycling of Non-ferrous Metals, Lanzhou University of Technology, Lanzhou; 730050, China; 3.School of Materials Science and Engineering, Lanzhou University of Technology, Lanzhou; 730050, China;
推荐引用方式 GB/T 7714	Hu, Ande,Wu, Lijian,Huang, Jiankang,et al. Recognition of weld defects from X-ray images based on improved convolutional neural network[J]. Multimedia Tools and Applications,2022,81(11):15085-15102.
APA	Hu, Ande,Wu, Lijian,Huang, Jiankang,Fan, Ding,&Xu, Zhenya.(2022).Recognition of weld defects from X-ray images based on improved convolutional neural network.Multimedia Tools and Applications,81(11),15085-15102.
MLA	Hu, Ande,et al."Recognition of weld defects from X-ray images based on improved convolutional neural network".Multimedia Tools and Applications 81.11(2022):15085-15102.