Volume 26 Issue 6
Jun.  2026
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WANG Si-yu, FANG Hong-su, YANG Wei, ZHOU Yong-jun. Road pothole segmentation network based on multi-scale feature fusion and channel feature adaptation[J]. Journal of Traffic and Transportation Engineering, 2026, 26(6): 167-185. doi: 10.19818/j.cnki.1671-1637.2026.031
Citation: WANG Si-yu, FANG Hong-su, YANG Wei, ZHOU Yong-jun. Road pothole segmentation network based on multi-scale feature fusion and channel feature adaptation[J]. Journal of Traffic and Transportation Engineering, 2026, 26(6): 167-185. doi: 10.19818/j.cnki.1671-1637.2026.031
shu

Road pothole segmentation network based on multi-scale feature fusion and channel feature adaptation

doi: 10.19818/j.cnki.1671-1637.2026.031
  • 1.

    School of Automobile, Chang'an University, Xi'an 710064, Shaanxi, China

  • 2.

    School of Highway, Chang'an University, Xi'an 710064, Shaanxi, China

Funds:

National Key R&D Program of China 2021YFB2601000

Key R&D Program of Shaanxi Province 2024CY2-GJHX-31

More Information
  • Corresponding author: YANG Wei, lecturer, PhD, E-mail: yw@chd.edu.cn
  • Received Date: 2025-01-12
  • Accepted Date: 2025-06-05
  • Rev Recd Date: 2025-03-27
  • Publish Date: 2026-06-28
    Abstract
  • To accurately identify road potholes and the improve model generalization segmentation ability, a road pothole segmentation network (potholes-FBConvNet) based on astrous spatial pyramid fusion (ASPF) and feature adaptation was proposed. The convolutional neural network model (ConvNext) was used as the main structure for multi-scale feature extraction at different network depths. The unified perception and resolution network architecture (UPerNet) was used as the neck fusion network. The multi-level feature representations extracted through the main network were fully utilized, and the ASPF module was introduced into the neck network to further enhance its ability to capture multi-scale contextual feature information. A feature adaptation module was embedded between the neck and decoding head to perform adaptive calibration on the channel and spatial features before neck fusion feature decoding. In this way, the network segmentation performance was effectively improved. A total of 2 097 road pothole segmentation databases were separately collected and meticulously annotated (1 065 generally damaged potholes, 507 completely damaged potholes, 525 severely damaged potholes), and comparative and ablation experiments were conducted in sequence to verify network performance. The experimental results show that, the proposed potholes-FBConvNet model achieves an intersection over union (IoU), similarity coefficient, precision, and recall of 85.43%, 92.14%, 92.96%, and 91.33% on generally damaged potholes. On completely damaged potholes, it reaches 87.76%, 93.48%, 92.33%, and 94.67%. On severely damaged potholes, it reaches 90.76%, 95.15%, 95.41%, and 94.90%. Compared with 16 typical comparison models based on Transformer and Conv, the proposed segmentation network has the optimal generalization segmentation ability and robustness.

     

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