Abstract
With the continuous improvement of deep learning algorithms, it has achieved considerable results in the fields of machine vision and natural language processing. In the face of problems that cannot be solved by traditional methods or are not effective, deep learning can achieve more desirable results through its powerful feature learning and mastering the laws of the problem. The lane recognition based on a convolutional neural network is proposed to address the problems of lane crushing of self-driving intelligent vehicles and the accuracy of traffic marker recognition, respectively. The traffic marker recognition model based on YOLO5, by collecting data and completing model training, finally achieves the average lane crush of the self-driving car less than 1 time, and the traffic marker recognition rate reaches 98.5%.
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Acknowledgements
Funding: This work was supported by the Education Department of Jiangxi Province of China [Grant Number GJJ204911 and Number GJJ204910]; the Science and Technology Bureau of Ganzhou City of China [Grant Number [2018]50]; the Enducation Department of Jiangxi Province of China Science and Technology research projects [Grant Number JXJZ-20-52-12].
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Tao, X., Li, H., Deng, L. (2022). Research on Self-driving Lane and Traffic Marker Recognition Based on Deep Learning. In: Li, K., Liu, Y., Wang, W. (eds) Exploration of Novel Intelligent Optimization Algorithms. ISICA 2021. Communications in Computer and Information Science, vol 1590. Springer, Singapore. https://doi.org/10.1007/978-981-19-4109-2_12
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DOI: https://doi.org/10.1007/978-981-19-4109-2_12
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