Journal of Shandong University(Engineering Science) ›› 2025, Vol. 55 ›› Issue (3): 72-79.doi: 10.6040/j.issn.1672-3961.0.2024.092

• Transportation Engineering—Special Issue for Intelligent Transportation • Previous Articles    

Deep learning-based intelligent judgment for radar detection of pavement cracks

DONG Mingshu1, CHEN Liqi1, MA Chuanyi2, ZHANG Zhuhao1, SUN Renjuan1, GUAN Yanhua1, ZHUANG Peizhi1*   

  1. DONG Mingshu1, CHEN Liqi1, MA Chuanyi2, ZHANG Zhuhao1, SUN Renjuan1, GUAN Yanhua1, ZHUANG Peizhi1*(1. School of Qilu Transportation, Shandong University, Jinan 250002, Shandong, China;
    2. Shandong Hi-Speed Group Co., Ltd., Jinan 250098, Shandong, China
  • Published:2025-06-05

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Abstract: This study employed GPR(ground penetrating radar)to identify and locate surface cracks in typical road segments. The method combined core drilling sampling and milling observation for validation, resulting in the construction of a database containing 728 radar images. The YOLO v8l algorithm was used to learn crack features. By incorporating an attention mechanism and modifying the activation function within the YOLO v8l framework, the study overcame the interference caused by the variability of road crack image features and significant noise, while also eliminating model overfitting. After modifying the algorithm, the model's computational parameters increased, and the computational efficiency improved. The precision and recall rates of the revised algorithm reached 99.4% and 92.3%, respectively. During training, the mean average precision and loss function fluctuations were minimal, indicating that the dataset annotation principles were consistent. This proved the effectiveness and reliability of the proposed method for identifying road surface cracks.

Key words: ground penetrating radar, artificial intelligence, object detection, deep learning algorithm, convolutional neural network

CLC Number: 

  • U418.6
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