Abstract
With more and more developed technology, unmanned driving technology has gradually entered people’s vision. Many cars are now equipped with self-parking technology, which allows the vehicle to enter the garage through a pre-set path without manual operation. At the same time, the traffic light identification mentioned in this paper can also be used for driverless cars, and can also be combined with manual driving to reduce the probability of traffic accidents caused by running red lights. This design is based on Transformer and Resnet convolutional neural network, using feature extraction technology. First of all, the data set used in this algorithm is self-made COCO data set. Then, the corresponding training is carried out to obtain the corresponding model of traffic signal light recognition. Finally, the trained model is used to predict the relevant traffic signal light.
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Acknowledgement
This work was supported by the National Natural Science Foundation of China (61772179), Hunan Provincial Natural Science Foundation of China (2022JJ50016,2020JJ4152), the Science and Technology Innovation Program of Hunan Province (2016TP1020), Scientific Research Fund of Hunan Provincial Education Department (21B0649), Double First-Class University Project of Hunan Province (Xiangjiaotong [2018]469), Postgraduate Scientific Research Innovation Project of Hunan Province (CX20190998), Degree and Postgraduate Education Reform Project of Hunan Province (2019JGYB266, 2020JGZD072), Industry University Research Innovation Foundation of Ministry of Education Science and Technology Development Center (2020QT09), Hengyang Technology Innovation Guidance Projects (2020h052805, Hengcaijiaozhi [2020]-67), Postgraduate Teaching Platform Project of Hunan Province (Xiangjiaotong [2019]370-321).
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Ou, Y., Sun, Y., Yu, X., Yun, L. (2022). Traffic Signal Light Recognition Based on Transformer. In: Liu, Q., Liu, X., Cheng, J., Shen, T., Tian, Y. (eds) Proceedings of the 12th International Conference on Computer Engineering and Networks. CENet 2022. Lecture Notes in Electrical Engineering, vol 961. Springer, Singapore. https://doi.org/10.1007/978-981-19-6901-0_143
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DOI: https://doi.org/10.1007/978-981-19-6901-0_143
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