Intelligent detection method for submarine pipelines based on Faster R-CNN

doi:10.16265/j.cnki.issn1003-3033.2023.06.2360

Abstract

Abstract:

In order to improve the detection accuracy of submarine pipeline defects and components and to realize intelligent submarine pipeline safety detection, a Faster R-CNN-based intelligent detection method for submarine pipelines was proposed. Firstly, the key features of the signals were enhanced by converting the signals into a pseudo-color map through basis value correction and segmentation mapping-pseudo-colorization methods. Secondly, the generalization capability of the detection model was improved based on multimodal data enhancement. Then, the improved Faster R-CNN network was trained based on the samples after multimodal data enhancement to establish the optimal intelligent detection model. Finally, the effectiveness of the proposed method was verified by using the test site and the Bohai Sea in service as examples. The results show that the average detection accuracy of the proposed method can reach 93.8%, which is 8% better than the original Faster R-CNN algorithm, and the average intersection over union reaches 0.75, which can accurately achieve the multi-target detection of submarine oil and gas pipelines and ensure the safe operation of submarine pipelines.

Key words: Faster region-convolutional neural network(Faster R-CNN), submarine pipelines, intelligent detection, magnetic flux leakage internal inspection

YU Jin, TANG Jianhua, SHEN Xiangkai, LIU Jinhai. Intelligent detection method for submarine pipelines based on Faster R-CNN[J]. China Safety Science Journal, 2023, 33(6): 80-87.

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缺陷	人工标注坐标	Faster R-CNN算法	本文算法	IoU1	IoU2
2	[108,33,155,61]	[112,41,158,64]	[110,37,155,59]	0.57	0.75
3	[178,31,249,64]	[184,36,245,63]	[181,34,251,62]	0.71	0.79

缺陷	人工标注坐标	Faster R-CNN算法	本文算法	IoU1	IoU2
1	[25,32,63,46]	[23,32,64,50]	[25,31,63,48]	0.72	0.82
2	[58,47,97,61]	[61,50,94,64]	[56,47,99,63]	0.56	0.79
3	[83,31,112,43]	[82,33,113,45]	[82,32,114,46]	0.67	0.67
4	[113,30,129,42]	[114,30,126,46]	[117,31,128,42]	0.60	0.63
5	[130,29,145,42]	[127,31,142,43]	[128,31,147,44]	0.54	0.60