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Title |
Estimation of Bridge Vehicle Loading using CCTV images and Deep Learning
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Authors |
배숙경(Suk-Kyoung Bae) ; 정우영(Wooyoung Jeong) ; 최수현(Soohyun Choi) ; 김병현(Byunghyun Kim) ; 조수진(Soojin Cho) |
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DOI |
https://doi.org/10.11112/jksmi.2024.28.3.10 |
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Keywords |
딥러닝; Faster R-CNN; 차량하중; 교량; CCTV Deep Learning; Faster R-CNN; Vehicle Loading; Bridge; CCTV |
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Abstract |
Vehicle loading is one of the main causes of bridge deterioration. Although WiM (Weigh in Motion) can be used to measure vehicle loading on a bridge, it has disadvantage of high installation and maintenance cost due to its contactness. In this study, a non-contact method is proposed to estimate the vehicle loading history of bridges using deep learning and CCTV images. The proposed method recognizes the vehicle type using an object detection deep learning model and estimates the vehicle loading based on the load-based vehicle type classification table developed using the weights of empty vehicles of major domestic vehicle models. Faster R-CNN, an object detection deep learning model, was trained using vehicle images classified by the classification table. The performance of the model is verified using images of CCTVs on actual bridges. Finally, the vehicle loading history of an actual bridge was obtained for a specific time by continuously estimating the vehicle loadings on the bridge using the proposed method.
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