Abstract
Due to factors such as vehicle overload, temperature changes and natural aging, cracks, pits and other defects will inevitably appear on the road surface, and computer vision, deep learning and other technologies are comprehensively used to detect the road surface in real time. The acquired image is preprocessed, and the affine transformation is used to remove irrelevant background and obtain the region of interest where defects may exist. Threshold segmentation and morphological operations are used to construct feature operators of different defect types, compare the differences between the feature values of the road surface to be measured and the normal pavement and area, and classify the defects of the road surface to be measured. The YOLOv5 algorithm is used to pre-train the capsules on the manually labeled dataset, continue training using the obtained weights, and finally use the trained model to detect the newly acquired defect images.
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Acknowledgements
This work is supported by Shandong Province Small and Medium Enterprises Innovation Capability Improvement Project (2021TSGC1441); Construction Machinery Intelligent Equipment ln novation & Entrepreneurship Community (GTT20220211); Shandong SME Innovation Capacity Enhancement Project (2022TSGC1379).
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Men, T., Bao**Wang, Zhang, N., Sun, Q. (2024). Pavement Defect Detection Method Based on Deep Learning. In: Zhang, Y., Qi, L., Liu, Q., Yin, G., Liu, X. (eds) Proceedings of the 13th International Conference on Computer Engineering and Networks. CENet 2023. Lecture Notes in Electrical Engineering, vol 1126. Springer, Singapore. https://doi.org/10.1007/978-981-99-9243-0_49
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DOI: https://doi.org/10.1007/978-981-99-9243-0_49
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