Abstract
As global warming and urbanisation continue to accelerate, resulting in the increasing likelihood and uncertainty of extreme rainstorms and floods, how to detect things in flooding scenarios and implement relevant rescue measures has become an urgent problem to be solved. Flooding scenarios are difficult and dangerous to obtain data, which leads to relatively few data sets dedicated to the detection of the degree of danger of vehicles in flooding scenarios. To this end, a dataset for vehicle hazard detection in urban flooding is proposed and the YOLOv8s algorithm is improved to increase the detection accuracy. The proposed dataset aims to provide realistic, diverse and challenging vehicle images in flooding scenarios, including different flood hazard scenarios and time periods. The dataset contains a total of 20,152 images, which are divided into training, validation and test sets in the ratio of 8: 1: 1 and evaluated and validated on the existing target detection algorithms. The authenticity and accuracy of the dataset is ensured by collecting data from real flooding sites.
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Acknowledgments
This study was funded by the National Natural Science Foundation of China (Grant No. 62171042, 62102033), the R&D Program of Beijing Municipal Education Commission (Grant No. KZ202211417048), the Project of Construction and Support for high-level Innovative Teams of Beijing Municipal Institutions(Grant No. BPHR20220121), the National Key R&D Program of China (2022YFC3090603), the Beijing Natural Science Foundation(Grant No. 4232026, 4242020), the Academic Research Projects of Beijing Union University(No.ZKZD202302).
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Sun, J., Zhang, C., Xu, C., Wang, P., Liu, H. (2024). ISE-UFDS: A Dataset for Detecting the Degree of Danger to Vehicles in Urban Flooding and Performance Assessment. In: Huang, DS., Zhang, C., Zhang, Q. (eds) Advanced Intelligent Computing Technology and Applications. ICIC 2024. Lecture Notes in Computer Science, vol 14868. Springer, Singapore. https://doi.org/10.1007/978-981-97-5600-1_35
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