Abstract
Dangerous goods are harmful to human health and environment if not properly controlled. It is necessary to monitor the road transportation of dangerous goods in real time. In this paper, we provide a solution for the automated recognition of dangerous goods vehicles in road and achieve the state-of-art performance on the Dangerous Goods Vehicles datasets. We improve the existing faster RCNN by combining several strategies, including a lightweight CNN architecture for basic feature extracting, guided negative samples and a series of heuristic rules for false detection filtering. The experiment indicates that our solution improve both the accuracy and the speed of detecting dangerous goods vehicles.
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Acknowledgement
This work is sponsored by the National Key R&D Program of China (2017YFC0821603).
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Hou, Q., Wang, L. (2020). Recognition for Dangerous Goods Vehicles in Road Based on Dangerous Goods Mark Detection. In: Xu, Z., Parizi, R., Hammoudeh, M., Loyola-González, O. (eds) Cyber Security Intelligence and Analytics. CSIA 2020. Advances in Intelligent Systems and Computing, vol 1147. Springer, Cham. https://doi.org/10.1007/978-3-030-43309-3_13
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DOI: https://doi.org/10.1007/978-3-030-43309-3_13
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