Abstract
Highway pavement cracks are the main factors affecting traffic safety, among which asphalt pavement cracks are the main research object. Therefore, the ability to detect cracks accurately and quickly becomes an important research object in pavement identification. In this paper, a method of crack detection for highway asphalt pavement is presented using deep learning. First, the Retinex image enhancement algorithm is applied to the dimmer and lower contrast images in the dataset, so that a brighter and higher contrast image dataset can be obtained. Secondly, by introducing the yolov5 algorithm and classifying the data set cracks and traffic signal lines, 1500 datasets are trained and validated with 200 validation sets. The whole training model was evaluated with mAP (mean Average Precision) and P-R curve as evaluation index. The final training result shows that the crack recognition rate is 86.7%, the ground traffic line is 91.3%, and the mAP is stable at about 0.8. Therefore, the identification algorithm designed in this paper can meet the requirements of crack detection, and has a high accuracy, which has a guiding significance for the maintenance and protection of pavement.
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References
Tang, Y.: Research on Intelligent Identification Algorithm of Asphalt Pavement Crack Based on Convolutional Neural Network. Southwest Jiaotong University (2021)
Chen, L.: Research on Pavement Crack Detection Based on Deep Learning. Nanjing University of Posts and Telecommunications (2021)
Wang, H.: Research on Road Crack Detection Method Based on Machine Learning. Shenyang University of Architecture (2021)
Li, Y., Xu, X., Li, S.: Research on intelligent recognition method of asphalt pavement disease image based on deep learning. Transp. Technol. (2022)
Le Cun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015)
Xu, K.: Research on Pavement Crack Detection and Extraction Method Based on YOLOv5s. Chang’an University (2022)
Wang, H., Song, M., Cheng, C.: Road target detection method based on improved YOLOv5 algorithm. J. Jilin Univ. (Eng. Ed.), 1–9 (2023)
Wu, R., Bai, Y., Han, J.: A method for detecting and identifying highway pavement damage based on deep learning. Comput. Simul. 40(01), 208–212 (2023)
Li, X.: Highway Pavement Crack Detection Based on Deep Learning. Huazhong University of Science and Technology (2021)
Yu, J.: Research on Highway Pavement Disease Detection Method Based on YOLO. East China Jiaotong University (2022)
Cheng, L.: Research on Pavement State Image Recognition Technology Based on Deep Learning. Shanghai University of Engineering and Technology (2020)
Zhang, W.: Research on Road Surface Image Crack Detection Algorithm Based on Deep Learning. Harbin Institute of Technology (2022)
Guo, C.: Research on Asphalt Pavement Disease Detection and Evaluation Methods Based on Deep Learning. Shijiazhuang Railway University (2022)
Zhao, X.: Research and Implementation of a Pavement Pits Detection System Based on Deep Learning. Ningxia University (2020)
Acknowledgement
This work was partially supported by the Supported by the Natural Science Foundation of Shandong Province (ZR2022MF267): Research on Road Surface Condition Recognition and Friction Estimation Methods in Autonomous Driving. We also wish to acknowledge the support of National Natural Science Foundation of China under Grant (Nos. 61903227).
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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Chen, X., Gao, H., Kong, T. (2023). Research on Crack Identification of Highway Asphalt Pavement Based on Deep Learning. In: Zhang, H., et al. International Conference on Neural Computing for Advanced Applications. NCAA 2023. Communications in Computer and Information Science, vol 1869. Springer, Singapore. https://doi.org/10.1007/978-981-99-5844-3_41
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DOI: https://doi.org/10.1007/978-981-99-5844-3_41
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