Road hazards (RH) have always been the cause of many serious traffic accidents. These have posed a threat to the safety of drivers, passengers, and pedestrians, and have also resulted in significant losses to people and even to the economies of countries. Hence, road hazards detection (RHD) could play an essential role in intelligent transportation systems (hypertarget ITSITS). The cooperative vehicle-infrastructure systems (CVIS) coordinate the communication between vehicles and roadside infrastructures. Onboard computing devices (OCD), then, make fast analyses and decisions based on road conditions. In this study, an RHD solution based on CVIS is proposed. Firstly, a high-performance heavy action detection model is selected. Using a meta-learning paradigm, critical features are generalized from a few-shot RH data. Secondly, we designed a lightweight RHD model to ensure its smooth inference on an OCD. Thirdly, we use a knowledge distillation (KD) framework to progressively distill the features of the complex model and the privileged information of the data into the lightweight one. Experimental results demonstrate that the model can effectively detect RH and obtain an accuracy of 90.2% with an inference time of 14.7ms.
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Project (external):
National Key Research and Development Program of China National Natural Science Foundation of China National Natural Science Foundation of China National Natural Science Foundation of China National Natural Science Foundation of China Key Research and Development Plan of Shaanxi Province Key Research and Development Plan of Shaanxi Province Key Research and Development Plan of Shaanxi Province Natural Science Foundation of Guangdong Province of China Key Project on Artificial Intelligence of Xi’an Science and Technology Plan Key Project on Artificial Intelligence of Xi’an Science and Technology Plan Key Project on Artificial Intelligence of Xi’an Science and Technology Plan Xi’an Science and Technology Plan Xi’an Key Laboratory of Mobile Edge Computing and Security
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Project ID:
Grant 2020YFB1807500 Grant 62072360 Grant 62001357 Grant 62172438 Grant 61901367 Grant 2021ZDLGY02-09 Grant 2023-GHZD-44 Grant 2023-ZDLGY-54 Grant 2022A1515010988 Grant 2022JH-RGZN-0003 Grant 2022JH-RGZN-0103 Grant 2022JH-CLCJ-0053 Grant 20RGZN0005 Grant 201805052-ZD3CG36