基于TSCD模型的轨道板裂缝检测方法

doi:10.3969/j.issn.0255-8297.2022.01.014

Abstract

Abstract: In order to solve the problem of track slab crack detection, a track slab crack detection model based on branch cascaded convolutional neural network, TSCD, is proposed. First, the model highlights the position information of track slab cracks through attention mechanism and structure of search branches to suppress interference information. Second, it realizes the pixel-level detection of cracks by structure of detecting branches. Finally, in order to solve the problem of image detail degradation in detection results, parameter mapping is used to achieve up-sampling of the feature maps. Experimental results show that the proposed model in this paper can not only detect the cracks in track plate surface images accurately with pixel accuracy rate of 97.56% and F1-score of 86.28%, but also performs strong generalization in cross-dataset tests.

Key words: crack detection, branch cascade, parameter mapping, track plate, convolutional neural network

CLC Number:

TN911.73

LI Wenju, ZHANG Yaoxing, CHEN Huiling, LI Peigang, SHA Liye. Track Slab Crack Detection Method Based on TSCD Model[J]. Journal of Applied Sciences, 2022, 40(1): 155-166.

References

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[2] Fan D P, Ji G P, Sun G, et al. Camouflaged object detection[C]//Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020:2777-2787.
[3] 王登涛, 李再帏, 何越磊, 等. 基于热成像的高速铁路轨道板表面裂缝检测方法研究[J]. 铁道标准设计, 2020, 64(7):22-28. Wang D T, Li Z W, He Y L, et al. Research on surface crack detection method of high-speed railway track slab based on thermal imaging[J]. Railway Standard Design, 2020, 64(7):22-28. (in Chinese)
[4] 王登涛, 路宏遥, 孟翔震, 等. 轨道板表面裂缝的热成像检测效果仿真分析[J]. 智能计算机与应用, 2020, 10(2):132-137. Wang D T, Lu H Y, Meng X Z, et al. Simulation analysis of thermal imaging detection effect of track plate surface cracks[J]. Intelligent Computers and Applications, 2020, 10(2):132-137. (in Chinese)
[5] 章梦. 基于图像处理的轨道裂缝检测技术的研究[D]. 上海:上海应用技术大学, 2019.
[6] 薛峰, 赵丽科, 柴雪松, 等. 基于图像处理的铁路轨道板裂缝检测研究[J]. 铁道建筑, 2015(12):123-126. Xue F, Zhao L K, Chai X S, et al. Study on detecting crack in railway track slab based on image processing technology[J]. Railway Engineering, 2015(12):123-126. (in Chinese)
[7] 战友, 阳恩慧, 马啸天, 等. 无砟轨道板裂缝三维激光检测系统研发与算法验证[J]. 铁道学报, 2021, 43(7):114-120. Zhan Y, Yang E H, Ma X T, et al. Development and algorithm verification of a threedimensional laser inspection system for cracks in ballastless track slabs[J]. Journal of the China Railway Society, 2021, 43(7):114-120. (in Chinese)
[8] 沈子豪. 基于机器视觉的CRTS II型轨道板裂缝检测技术研究[D]. 上海:上海应用技术大学, 2020.
[9] Fang F, Li L, Gu Y, et al. A novel hybrid approach for crack detection[J]. Pattern Recognition, 2020, 107:107-118.
[10] 李良福, 马卫飞, 李丽, 等. 基于深度学习的桥梁裂缝检测算法研究[J]. 自动化学报, 2019, 45(9):1727-1742. Li L F, Ma W F, Li L, et al. Research on bridge crack detection algorithm based on deep learning[J]. Journal of Automatica Sinica, 2019, 45(9):1727-1742. (in Chinese)
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Track Slab Crack Detection Method Based on TSCD Model

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