An Algorithm for Liner Identification of Tunnel Radar Data Based on Critical Information Supervision

doi:10.11871/jfdc.issn.2096-742X.2023.05.013

Abstract

Abstract:

[Objective] This paper mainly explores new processing methods for ground-penetrating radar data parsing and infrastructure pathology detection. The current ground-penetrating radar, as a common non-destructive technical tool in bridge and tunnel defect detection, is facing the problem of difficult data parsing. Improving the accuracy of the parsing results has significant application value to the defect detection of transportation infrastructure. [Methods] The lining structure is represented as an identification object, decomposed into key points and curves. Key point detection is based on a bipartite heat map approach, with the help of "soft annotation" to speed up model convergence. The curve fitting module is implemented by neural network regression, incorporating a counteracting perturbation mechanism to resist image noise interference. [Results] The results show that the algorithm identifies a liner offset of 2.23-pixel points from the true one, a 1.24-pixel point improvement over the CenterNet network and a 0.71-pixel point improvement over the CornerNet network. [Conclusions] The proposed method has significantly improved resolution recognition results and has obvious application value.

Key words: ground penetrating radar, heatmap, lining line detection, anti-disturbance, curve fitting

SONG Heng, GENG Tianbao, WANG Dongjie, ZHANG Yisheng. An Algorithm for Liner Identification of Tunnel Radar Data Based on Critical Information Supervision[J]. Frontiers of Data and Computing, 2023, 5(5): 154-163, https://cstr.cn/32002.14.jfdc.CN10-1649/TP.2023.05.013.

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	CenterNet	CornerNet	本文方法
欧式距离	6.33	5.72	3.76
曼哈顿距离	8.48	7.82	5.27
切比雪夫距离	5.67	4.95	3.01