Abstract
Intelligent driving can collect environmental information through on-board sensors and actively control the driving operation of vehicles to replace drivers. In order to improve driving safety and promote the construction of Intelligent Transportation System (ITS), this article improves the traditional Deep Learning (DL) method and puts forward an intelligent recognition model of automobile state based on digital image processing to assist the path tracking control of intelligent vehicles. The experimental results show that this algorithm has a good effect on intelligent recognition of automobile state and has a certain resistance to target occlusion. Compared with the contrast algorithm, the accuracy of the proposed model for intelligent identification of automobile state is improved by 27.11%, and the error is reduced by 38.75%, so that the driving state of the vehicle in front of the automobile can be located more accurately. Therefore, the image processing algorithm designed in this article can accurately track the target in the visual image of smart car, and has good robustness and real-time performance.
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This chapter was supported by the General Project of Natural Science Research in Higher Education Institutions in Jiangsu Province—A Research based on a Safe Search Vehicle Controlled by Raspberry Pi.
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Zhang, D. (2024). Construction of Intelligent Recognition System of Automobile State Based on Digital Image Processing. In: Kountchev, R., Patnaik, S., Nakamatsu, K., Kountcheva, R. (eds) Proceedings of International Conference on Artificial Intelligence and Communication Technologies (ICAICT 2023). ICAICT 2023. Smart Innovation, Systems and Technologies, vol 368. Springer, Singapore. https://doi.org/10.1007/978-981-99-6641-7_9
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DOI: https://doi.org/10.1007/978-981-99-6641-7_9
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