Gabor滤波融合卷积神经网络的路面裂缝检测方法

陈晓冬; 艾大航; 张佳琛; 蔡怀宇; 崔克让

doi:10.37188/CO.2020-0041

Volume 13 Issue 6

Dec. 2020

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Chinese Optics > 2020 > 13(6): 1293-1301.

CHEN Xiao-Dong, AI Da-Hang, ZHANG Jia-Chen, CAI Huai-Yu, CUI Ke-Rang. Gabor filter fusion network for pavement crack detection[J]. Chinese Optics, 2020, 13(6): 1293-1301. doi: 10.37188/CO.2020-0041

Citation:

CHEN Xiao-Dong, AI Da-Hang, ZHANG Jia-Chen, CAI Huai-Yu, CUI Ke-Rang. Gabor filter fusion network for pavement crack detection[J]. Chinese Optics, 2020, 13(6): 1293-1301. doi: 10.37188/CO.2020-0041

CHEN Xiao-Dong, AI Da-Hang, ZHANG Jia-Chen, CAI Huai-Yu, CUI Ke-Rang. Gabor filter fusion network for pavement crack detection[J]. Chinese Optics, 2020, 13(6): 1293-1301. doi: 10.37188/CO.2020-0041

Citation:

CHEN Xiao-Dong, AI Da-Hang, ZHANG Jia-Chen, CAI Huai-Yu, CUI Ke-Rang. Gabor filter fusion network for pavement crack detection[J]. Chinese Optics, 2020, 13(6): 1293-1301. doi: 10.37188/CO.2020-0041

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Gabor filter fusion network for pavement crack detection

doi: 10.37188/CO.2020-0041

1.
Key Laboratory of Photoelectric Information, Ministry of Education, School of Precision Instrument and Optoelectronics Engineering, Tianjin University, Tianjin 300072, China
2.
Tianjin Highway Engineering Design and Research Institute, Tianjin 300201, China

Funds: Transportation Technology Development Project of Tianjin (No. 2019-03)

More Information

Corresponding author: aidh@tju.edu.cn
Received Date: 16 Mar 2020
Rev Recd Date: 07 May 2020

Available Online: 22 Oct 2020

Publish Date: 01 Dec 2020

Abstract

Abstract

In pavement detection, the small sample of road crack image data makes it difficult for neural networks to extract useful features from images. To solve this problem, this paper proposes a Gabor Filter Convolutional Neural Network (GF-CNN) model. The GF-CNN model first inputs a road surface image into a small parameter prediction network, adaptively selects the parameters of the Gabor filter bank according to the input, and constructs a filter bank according to the predicted parameters, and then filters the initial road surface image to obtain the Gabor texture feature map. The texture feature map is inputted into a feature classification network constructed by the residual network to extract deep features, at the same time, to judge whether a crack exists. Test results on the GAPs pavement image dataset show that the F1 score of the GF-CNN model reaches 0.7137, which is superior to other pavement image detection methods. This model improves the feature extraction ability of CNNs by fusing texture features, and reduces the sensitivity of Gabor filter parameters to improve its ability to make generalizations. It has good applicability to pavement crack image detection.
- convolutional neural network,
- Gabor filter,
- crack detection,
- texture feature

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References(29)

References

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