Abstract
With the development of urbanization and the ongoing big data scene, intelligent object detection in traffic scenes has become a hot issue at the moment. In this paper, the largest computer vision algorithm evaluation data set in the world, the KITTI data set, is trained on the model of YOLOv3. K-means method is used to adjust anchor parameters to achieve more accurate results. The experimental results show that the traffic scene detection model trained in this paper has good detection effect.
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Acknowledgements
The research work in this paper was supported by the National Key Research and Development Program of China (No. 2018AAA0100203) and National Key R & D program of China (No. 2017YFC1502505). Professor Xin Zheng is the author to whom all correspondence should be addressed.
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Yin, Q., Yang, R., Zheng, X. (2021). Traffic Scene Detection Based on YOLOv3. In: Ahram, T. (eds) Advances in Artificial Intelligence, Software and Systems Engineering. AHFE 2020. Advances in Intelligent Systems and Computing, vol 1213. Springer, Cham. https://doi.org/10.1007/978-3-030-51328-3_22
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DOI: https://doi.org/10.1007/978-3-030-51328-3_22
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