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SPCNet: a strip pyramid ConvNeXt network for detection of road surface defects

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Abstract

Road surface defect detection plays an important role in the construction and maintenance of roads. However, the irregularity of road surface defects and the complexity of the background make the extraction of road surface defects very difficult. It is a challenge to extract the road surface defects accurately. To cope with this challenge, we introduce the theory of image segmentation in deep learning. However, existing deep learning networks suffer from insufficient segmentation accuracy, low model robustness, and a lack of generalization ability. Consequently, we propose a novel deep learning network named Strip Pyramid ConvNeXt Network for detecting road surface defects. Firstly, we introduced ConvNeXt as the encoder to ensure the segmentation accuracy of the model. Furthermore, we designed a strip pyramid pooling module with excellent edge detail extraction capability and a multi-feature fusion module. We also created a cementation fissure dataset (CE dataset) to test the accuracy of the model and verify the generalization capability and robustness of the model. Finally, we compared our model with ten advanced segmentation networks in recent years on CRACK500 dataset, GAPs384 dataset, and cementation fissure dataset (CE dataset), and our model outperforms others on four metrics.

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Data availability

The datasets analyzed during the current study are available from the corresponding author on reasonable request.

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Funding

This work was supported by the National Natural Science Foundation of China (51805078), the Fundamental Research Funds for the Central Universities (N2103011), the Central Guidance on Local Science and Technology Development Fund (2022JH6/100100023), and the 111 Project (B16009). Deep learning based highway defect detection algorithm research (S202210145181) Supported by National Training Program of Innovation and Entrepreneurship for Undergraduates.

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Authors and Affiliations

  1. School of Mechanical Engineering and Automation, Northeastern University, Shenyang, Liaoning, China

    Ziang Zhou, Wensong Zhao, Jun Li & Kechen Song

  2. National Frontiers Science Center for Industrial Intelligence and Systems Optimization, Northeastern University, Shenyang, 110819, China

    Ziang Zhou, Wensong Zhao, Jun Li & Kechen Song

  3. Key Laboratory of Data Analytics and Optimization for Smart Industry (Northeastern University), Ministry of Education, Shenyang, China

    Ziang Zhou, Wensong Zhao, Jun Li & Kechen Song

Authors
  1. Ziang Zhou
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  2. Wensong Zhao
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  3. Jun Li
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  4. Kechen Song
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Contributions

Ziang Zhou and Wensong Zhao wrote the main manuscript text and Jun Li prepared figures 1-7, Kechen Song prepared figures 8-10. All authors reviewed the manuscript.

Corresponding author

Correspondence to Kechen Song.

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Zhou, Z., Zhao, W., Li, J. et al. SPCNet: a strip pyramid ConvNeXt network for detection of road surface defects. SIViP 18, 37–45 (2024). https://doi.org/10.1007/s11760-023-02698-6

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  • DOI: https://doi.org/10.1007/s11760-023-02698-6

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