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
shu

Gabor filter fusion network for pavement crack detection

  • 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
  • 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.

     

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