Semi-Supervized Crack-Detection Method Based on Image-Semantic Segmentation

Liu Pei; Huang Yaping

doi:10.3788/LOP202158.0615004

Journals >Laser & Optoelectronics Progress >Volume 58 >Issue 6 >Page 615004 > Article

Laser & Optoelectronics Progress
Vol. 58, Issue 6, 615004 (2021)

Semi-Supervized Crack-Detection Method Based on Image-Semantic Segmentation

Liu Pei^1、2 and Huang Yaping^1、2、*

Author Affiliations

¹Beijing Key Lab of Traffic Data Analysis and Mining, School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China

²School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China

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DOI: 10.3788/LOP202158.0615004 Cite this Article Set citation alerts

Liu Pei, Huang Yaping. Semi-Supervized Crack-Detection Method Based on Image-Semantic Segmentation[J]. Laser & Optoelectronics Progress, 2021, 58(6): 615004 Copy Citation Text

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Fig. 1. Semi-supervised training framework

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Fig. 2. MSCM structure

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Fig. 3. Improved network framework

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Fig. 4. Pseudo tags obtained by different methods. (a) Original images; (b) ground-truth; (c) SF method; (d) wCtr method; (e) GC method

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Fig. 5. Crack-detection effect under different networks. (a) Original images; (b) ground-truth; (c) SegNet network; (d) DeepCrack network; (e) proposed network

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Fig. 6. Cack detection effect after training with different proportion of manual tag and pseudo tag. (a) Original images; (b) ground-truth; (c) 0; (d) 1/150; (e) 1/65; (f) 1/30; (g) 1/15; (h) 1/6; (i) 1

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Fig. 7. Detection effect of different cracks in different networks under full supervision. (a) Original images; (b) ground-truth; (c) SegNet network; (d) DeepCrack network; (e) proposed network

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Network	Precision /%	Recall /%	F1-score
SegNet^[19]	85.23	89.77	86.74
DeepCrack^[14]	81.71	92.88	86.26
Proposed network	80.29	93.56	85.66

Table 1. Crack-detection results of different networks

Method	Precision /%	Recall /%	F1-score
SF^[16]	76.87	72.37	69.22
wCtr^[17]	74.33	71.30	67.34
GC^[18]	86.57	66.93	70.00

Table 2. Quantitative analysis results of three methods

Proportion	Precision /%	Recall /%	F1-score
0	72.93	85.21	74.52
1/150	72.74	88.62	76.59
1/65	77.48	88.50	80.47
1/30	77.68	92.67	83.39
1/15	80.29	93.56	85.66
1/6	85.99	92.17	88.42
1	89.34	97.48	92.31

Table 3. Quantitative analysis results of manual labeled dataset and pseudo labeled dataset in different proportions

Proportion	Precision /%	Recall /%	F1-score
1/150	14.26	50.35	21.46
1/65	25.85	60.38	35.04
1/30	49.83	63.80	54.81
1/15	69.48	83.51	75.21
1/6	82.16	91.59	86.22

Table 4. Quantitative analysis results after pre-training on different proportions of manually annotated datasets

Fusion coefficient	Precision /%	Recall /%	F1-score
1∶0∶0	81.78	89.81	84.81
0∶1∶0	81.07	88.14	83.68
0∶0∶1	85.32	95.03	89.34
1∶1∶1	80.29	93.56	85.66
1∶1∶2	85.93	91.33	87.69
Union	80.39	91.67	84.77

Table 5. Crack detection results of SF, wCtr and GC methods under different fusion coefficients

Network	Precision /%	Recall /%	F1-score
SegNet ^[19]	85.23	89.77	86.74
DeepCrack^[14]	81.71	92.88	86.26
Proposed network	89.34	96.48	93.31

Table 6. Crack detection results of different networks under full supervision

Liu Pei, Huang Yaping. Semi-Supervized Crack-Detection Method Based on Image-Semantic Segmentation[J]. Laser & Optoelectronics Progress, 2021, 58(6): 615004

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