Volume 40 Issue 3
Jun.  2022
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Article Navigation >  Journal of Transport Information and Safety  > 2022  >  40(3): 127-135
ZHANG Zhihua, DENG Yanxue, ZHANG Xinxiu. A Method for Detecting and Differentiating Asphalt Pavement Distress Based on an Improved SegNet Algorithm[J]. Journal of Transport Information and Safety, 2022, 40(3): 127-135. doi: 10.3963/j.jssn.1674-4861.2022.03.013
Citation: ZHANG Zhihua, DENG Yanxue, ZHANG Xinxiu. A Method for Detecting and Differentiating Asphalt Pavement Distress Based on an Improved SegNet Algorithm[J]. Journal of Transport Information and Safety, 2022, 40(3): 127-135. doi: 10.3963/j.jssn.1674-4861.2022.03.013
shu

A Method for Detecting and Differentiating Asphalt Pavement Distress Based on an Improved SegNet Algorithm

doi: 10.3963/j.jssn.1674-4861.2022.03.013
  • 1.

    Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, China

  • 2.

    National-Local Joint Engineering Research Center of Technologies and Applications for National Geographic State Monitoring, Lanzhou Jiaotong University, Lanzhou 730070, China

  • 3.

    Gansu Provincial Engineering Laboratory for National Geographic State Monitoring, Lanzhou 730070, China

  • 4.

    Key Laboratory of Highway Network Monitoring in Gansu Province, Lanzhou 730070, China

  • Received Date: 2022-04-22
    Available Online: 2022-07-25
    Abstract
  • The existing SegNet methods are unable to accurately differentiate asphalt pavement distress with similar characteristics, such as cracks and sealed cracks. To solve this problem, a method of detecting asphalt pavement distress based on an improved SegNet network is proposed. In order to remove the negative impact of road markings and uneven illumination onto image quality of road surface and subsequent failure detection, a multi-scale Retinex algorithm with color restoration(MSRCR)is used to reduce the impact of road marking and uneven lighting on image quality. Through enhancing the contrast, hue and brightness of the images for road surface, the accuracy of distress recognition is improved. In order to fully use of the contextual information of the image, and overcome the issue with the SegNet network of being ineffective in segmenting and identifying subtle diseases, a residual neural network(ResNet)is introduced as the encoder, and two feature maps with a same scale obtained by a convolutional layer with a 1×1 kernel and a dilated convolutional layer with different dilation rates are fused for each feature map, generated by up-sampling in the decoder. And a closed, morphological operation is used to connect discontinuous cracks. To verify the effectiveness of the improved algorithm, it is compared with the classic semantic segmentation methods(such as SegNet and BiSeNet)over the test sets. The average intersection over Union(MIoU)and F1 score are(82.4%, 98.9%), (69.4%, 94.0%)and(80.5%, 98.1%), respectively. The three methods are compared in terms of their extraction efficiency in identifying pavement diseases using the pavement images collected at several freeway sections in the Gansu Province. The misdetection rate and false detection rate of cracks of the proposed method are 2.91%, 1.94%, respectively, which are much better than those of the SegNet(10.68%, 14.56%)and BiSeNet(6.80%, 12.62%). The above results show that the proposed method can be used to extract and identify asphalt pavement cracks and sealed cracks with a higher accuracy.

     

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