Intelligent detection method of tunnel cracks based on improved Mask R-CNN deep learning algorithm

doi:10.11996/JG.j.2095-302X.2023010177

Abstract

Abstract:

Tunnel crack detection is an important task for the prevention of major disasters and daily maintenance of the tunnel, but traditional manual detection would incur huge workload and cannot meet practical needs. The deep learning neural network Mask R-CNN model was used for intelligent automatic detection of cracks, avoiding the time-consuming and labor-intensive manual detection. By adjusting the algorithm parameters and optimizing the model detection results, the Mask R-CNN model suitable for tunnel crack detection was obtained. For the results of automatic identification of cracks, the geometric characteristic parameters were further calculated. In order to make full use of the long and narrow bending characteristics of cracks and reflect the trend and basic shape of cracks, a calculation method of geometric characteristics of cracks based on skeleton extraction and function fitting was proposed. According to the crack skeleton, the crack trend could be obtained and the crack length could be calculated. Through function fitting, the function penetrating the narrow and long region of the crack could be obtained, and the width could be calculated according to the normal vector of the function. According to the calculation results of crack geometric parameters, combined with the requirements of crack width to be repaired specified in the specification, the automatic early warning of tunnel crack detection could be realized, providing technical support for the automatic detection of tunnel cracks.

Key words: tunnel crack, deep learning, Mask R-CNN, skeleton extraction

CLC Number:

ZHU Lei, LI Dong-biao, YAN Xing-zhi, LIU Xiang-yang, SHEN Cai-hua. Intelligent detection method of tunnel cracks based on improved Mask R-CNN deep learning algorithm[J]. Journal of Graphics, 2023, 44(1): 177-183.

Figures/Tables 11

Fig. 1 Crack dataset annotation ((a) Crack annotation; (b) Crack mask)

Fig. 2 Mask R-CNN network structure diagram

Fig. 3 Flow chart of fracture geometric parameter extraction algorithm

Fig. 4 Schematic diagram of crack image width

Table 1 Mask R-CNN Algorithm parameters

参数设置	相关说明
Batch size=2	批处理个数，此处为2
Epochs=200	训练200轮数据集
Learning_rate=0.004	学习率设置为0.004
Weight decay=0.000 1	权重衰减系数为0.000 1

Fig. 5 Mask R-CNN model training loss curve

Table 2 Confusion matrix

混淆矩阵		真实值
混淆矩阵		Positive	Negative
预测值	Positive	真正例(TP)	假正例(FP)
预测值	Negative	假反例(FN)	真反例(TN)

Fig. 6 Mask R-CNN model training mAP curve

Fig. 7 Partial detection results ((a) Original image; (b) Mask R-CNN segmentation results; (c) Global threshold binarization segmentation results)

Fig. 8 Crack skeletonize results ((a) Original image; (b) Crack mask; (c) Skeleton extraction results)

Fig. 9 Function fitting results of crack skeleton ((a) Function fitting effect 1; (b) Function fitting effect 2)

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