Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/198075
Title: Injurious or Noninjurious Defect Identification From MFL Images in Pipeline Inspection Using Convolutional Neural Network
Authors: Jian Feng;Fangming Li;Senxiang Lu;Jinhai Liu;Dazhong Ma
Year: 2017
Publisher: IEEE
Abstract: This paper proposes an injurious or noninjurious defect identification method from magnetic flux leakage (MFL) images based on convolutional neural network. Different from previous approaches, this method is fed by the MFL images instead of the features of the MFL measurements, and thus it can skip the procedure of feature extraction. Moreover, for convenience, a normalization layer is added to the front of model. In the convolution layers, the rectified linear units are employed as the activation functions to shorten the training period and improve the performance. In addition, two local response normalization layers are also embedded into the proposed structure. We demonstrate the performance of the proposed model using real MFL data collected from experimental pipelines. Benefited from the special structure of the proposed model, this method is robust for shift, scale, and distortion variances of input MFL images. We also present a comparative result of the proposed model and other methods. The results prove that the proposed method can achieve higher accuracy than the traditional approaches.
URI: http://localhost/handle/Hannan/198075
volume: 66
issue: 7
More Information: 1883,
1892
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7878530.pdf5.19 MBAdobe PDF
Title: Injurious or Noninjurious Defect Identification From MFL Images in Pipeline Inspection Using Convolutional Neural Network
Authors: Jian Feng;Fangming Li;Senxiang Lu;Jinhai Liu;Dazhong Ma
Year: 2017
Publisher: IEEE
Abstract: This paper proposes an injurious or noninjurious defect identification method from magnetic flux leakage (MFL) images based on convolutional neural network. Different from previous approaches, this method is fed by the MFL images instead of the features of the MFL measurements, and thus it can skip the procedure of feature extraction. Moreover, for convenience, a normalization layer is added to the front of model. In the convolution layers, the rectified linear units are employed as the activation functions to shorten the training period and improve the performance. In addition, two local response normalization layers are also embedded into the proposed structure. We demonstrate the performance of the proposed model using real MFL data collected from experimental pipelines. Benefited from the special structure of the proposed model, this method is robust for shift, scale, and distortion variances of input MFL images. We also present a comparative result of the proposed model and other methods. The results prove that the proposed method can achieve higher accuracy than the traditional approaches.
URI: http://localhost/handle/Hannan/198075
volume: 66
issue: 7
More Information: 1883,
1892
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7878530.pdf5.19 MBAdobe PDF
Title: Injurious or Noninjurious Defect Identification From MFL Images in Pipeline Inspection Using Convolutional Neural Network
Authors: Jian Feng;Fangming Li;Senxiang Lu;Jinhai Liu;Dazhong Ma
Year: 2017
Publisher: IEEE
Abstract: This paper proposes an injurious or noninjurious defect identification method from magnetic flux leakage (MFL) images based on convolutional neural network. Different from previous approaches, this method is fed by the MFL images instead of the features of the MFL measurements, and thus it can skip the procedure of feature extraction. Moreover, for convenience, a normalization layer is added to the front of model. In the convolution layers, the rectified linear units are employed as the activation functions to shorten the training period and improve the performance. In addition, two local response normalization layers are also embedded into the proposed structure. We demonstrate the performance of the proposed model using real MFL data collected from experimental pipelines. Benefited from the special structure of the proposed model, this method is robust for shift, scale, and distortion variances of input MFL images. We also present a comparative result of the proposed model and other methods. The results prove that the proposed method can achieve higher accuracy than the traditional approaches.
URI: http://localhost/handle/Hannan/198075
volume: 66
issue: 7
More Information: 1883,
1892
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7878530.pdf5.19 MBAdobe PDF