Bridge Crack Detection Algorithm Based on Image Processing under Complex Background

Liangfu Li; Ruiyun Sun

doi:10.3788/LOP56.061002

Journals >Laser & Optoelectronics Progress >Volume 56 >Issue 6 >Page 061002 > Article

Laser & Optoelectronics Progress
Vol. 56, Issue 6, 061002 (2019)

Bridge Crack Detection Algorithm Based on Image Processing under Complex Background

Liangfu Li^** and Ruiyun Sun^*

Author Affiliations

School of Computer Science, Shaanxi Normal University, Xi'an, Shaanxi 710119, China

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DOI: 10.3788/LOP56.061002 Cite this Article Set citation alerts

Liangfu Li, Ruiyun Sun. Bridge Crack Detection Algorithm Based on Image Processing under Complex Background[J]. Laser & Optoelectronics Progress, 2019, 56(6): 061002 Copy Citation Text

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Schematic of dataset amplification of bridge crack images. (a) Original image; (b) horizontal flip; (c) vertical flip; (d) linear transformation; (e) spatial filtering transformation

Fig. 1. Schematic of dataset amplification of bridge crack images. (a) Original image; (b) horizontal flip; (c) vertical flip; (d) linear transformation; (e) spatial filtering transformation

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Fig. 2. Generative model

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Fig. 3. Discriminant model

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Fig. 4. Schematic of 4-layer DenseBlock

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Fig. 5. Schematic of detection of high-resolution image

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Fig. 6. Visualization comparison of cracks generated by DCGAN and BCIGM. (a) Nepoch=01; (b) Nepoch=03; (c) Nepoch=16; (d) Nepoch=25

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Fig. 7. Visualization comparison of cracks generated by ReLU and SeLU. (a) Nepoch=01; (b) Nepoch=03; (c) Nepoch=16; (d) Nepoch=25

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Fig. 8. Visualization comparison of experimental results with and without dataset amplification. (a) Original image; (b)label; (c) without dataset amplification; (d) with dataset amplification

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Fig. 9. Comparison of crack detection results between existing algorithms and proposed algorithm. (a) Original image; (b) label; (c) threshold segmentation algorithm; (d) Canny algorithm; (e) NB-CNN algorithm; (f) random structure forest algorithm; (g) proposed algorithm

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Fig. 10. Partial crack detection results by proposed algorithm. (a) Scene 1; (b) scene 2; (c) scene 3

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Name oflayer	Size ofkernel /(pixel×pixel)	Stride /pixel	Size of output featuremap /(pixel×pixel)	Number offeature map
Inputlayer	-	-	256×256	3
Convolution	Convolution 5×5	1	256×256	48
DenseBlock	Convolution [3×3]×4	1	256×256	96
Transition Down	Convolution 1×1	1	256×256	96
Transition Down	Max pooling 2×2	2	128×128	96
DenseBlock	Convolution [3×3]×5	1	128×128	156
Transition Down	Convolution 1×1	1	128×128	156
Transition Down	Max pooling 2×2	2	64×64	156
DenseBlock	Convolution [3×3]×7	1	64×64	240
Transition Down	Convolution 1×1	1	64×64	240
Transition Down	Max pooling 2×2	2	32×32	240
DenseBlock	Convolution [3×3]×10	1	32×32	360
Transition Down	Convolution 1×1	1	32×32	360
Transition Down	Max pooling 2×2	2	216×16	360
DenseBlock	Convolution [3×3]×12	1	16×16	504
Transition Up	Deconvolution 3×3	2	32×32	504
DenseBlock	Convolution [3×3]×10	1	32×32	624
Transition Up	Deconvolution 3×3	2	64×64	624
DenseBlock	Convolution [3×3]×7	1	64×64	444
Transition Up	Deconvolution 3×3	2	128×128	444
DenseBlock	Convolution [3×3]×5	1	128×128	300
Transition Up	Deconvolution 3×3	2	256×256	300
DenseBlock	Convolution [3×3]×4	1	256×256	204
Convolution	Convolution 1×1	1	256×256	2
Softmax	-	-	-	-

Table 1. Network structure parameters of BCISM

Type ofpicture	Simplebackground	Backgroundwith obstacles	Backgroundwith largearea of stains
Number ofpictures	10449	13275	5710
Proportion /%	35.5	45.1	19.4

Table 2. Proportion of number of different types of images in total dataset

Condition	Time /s
Without 1×1 convolution kernel	5.4801
With 1×1 convolution kernel	5.4312

Table 3. Influence of BCIGM on training speed under different conditions

Number oftraining samples	With or withoutdataset amplification	Number ofverification samples	P_Precision /%	P_Recall /%
1183	Without dataset amplification	156	13.5	17.9
29434	With dataset amplification	156	92.9	92.6

Table 4. Effect of dataset amplification on experimental results

Model	Pre-training	Parameter /M	P_Precision /%	P_Recall /%	P_{F1_Score} /%	Time /s
SegNet	True	29.5	74.0	78.5	76.2	0.5823
FCN8	True	134.5	86.9	83.4	85.1	0.3739
DeepLab	True	37.3	82.6	80.9	81.7	0.9751
FC-DenseNet56 (k=12)	False	1.5	89.8	87.6	88.7	0.1685
FC-DenseNet67 (k=16)	False	3.5	89.0	88.8	88.9	0.2635
FC-DenseNet103 (k=16)	False	9.4	93.0	92.1	92.5	0.2795
BCISM (k=12)	False	2.8	92.9	92.6	92.8	0.1998

Table 5. Comparison of exiting semantic segmentation models and BCISM

Liangfu Li, Ruiyun Sun. Bridge Crack Detection Algorithm Based on Image Processing under Complex Background[J]. Laser & Optoelectronics Progress, 2019, 56(6): 061002

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