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Autonomous Driving Technology through Image Classfication and Object Recognition based on CNN

Published: 04 March 2021 Publication History

Abstract

The time has come for humans that the cars themselves think and judge rather than driving by humans, and has already reached a certain level. It hasn't reached the commercialization stage yet, but it's not a story of the future anymore, and even now, a great deal of technological development is taking place every day. However, since this autonomous driving technology does not have a driver when an accident occurs, it can cause problems in terms of responsibility and ethics, so it has to be possible to make the best choice at anytime and anywhere without accident. It requires a high level of technology on the software and the hardware. Most autonomous vehicles currently on the market do not stay or exceed the level of 2 to 2.5. The level of 2~2.5 is a partial automation step, and in a stable environment, autonomous driving is possible in part, but it must be possible for the driver to immediately take over control of the driving of the vehicle.
Using CNN-based YOLOv3 implements object recognition and image classification, which are the core technologies of autonomous driving technology and Based on the data, it is implemented through the AI autonomous vehicle kit, so that it is possible to make suggestion or ideas for development of Autonomous driving technology.

References

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Joseph Redmon, Santosh Divvala, Ross Girshick, Ali Farhadi, "You Only Look Once: Unified, Real-Time Object Detection," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 779--788
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Joseph Redmon, Santosh Divvala, Ross Girshick, Ali Farhadi, "You Only Look Once: Unified, Real-Time Object Detection," arXiv:1506.02640 [cs.CV]
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Zhong-Qiu Zhao, Peng Zheng, Shou-Tao Xu, Xindong Wu, "Object Detection With Deep Learning: A Review," IEEE Transactions on Neural Networks and Learning Systems (Volume: 30, Issue: 11, Nov. 2019)
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H. Noh and J.Heo, "Mutually Orthogonal Softmax Axes for Cross-Domain Retrieval," in IEEE Access, vol. 8, pp. 56491--56500, 2020.
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Hyun S., Heo JP. (2020) VarSR: Variational Super-Resolution Network for Very Low Resolution Images. In: Vedaldi A., Bischof H., Brox T., Frahm JM. (eds) Computer Vision - ECCV 2020. ECCV2020. Lecture Notes in Computer Science, vol 12368. Springer, Cham. https://doi.org/10.1007/978-3-030-58592-1_26
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Joseph Redmon, Ali Farhadi, "YOLOv3: An Incremental Improvement," arXiv:1804.02767 [cs.CV]
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H. Noh and J. Heo, "Mutually Orthogonal Softmax Axes for Cross-Domain Retrieval," in IEEE Access, vol. 8, pp. 56491--56500, 2020.
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Hyun S., Heo JP. (2020) VarSR: Variational Super-Resolution Network for Very Low Resolution Images. In: Vedaldi A., Bischof H., Brox T., Frahm JM. (eds) Computer Vision - ECCV 2020. ECCV 2020. Lecture Notes in Computer Science, vol 12368. Springer, Cham. https://doi.org/10.1007/978-3-030-58592-1_26

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  1. Autonomous Driving Technology through Image Classfication and Object Recognition based on CNN

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    ICVISP 2020: Proceedings of the 2020 4th International Conference on Vision, Image and Signal Processing
    December 2020
    366 pages
    ISBN:9781450389532
    DOI:10.1145/3448823
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Publication History

    Published: 04 March 2021

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    Author Tags

    1. Autonomous Driving
    2. CNN
    3. Image Classification
    4. Obstacle Recognition
    5. YOLOv3

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    Overall Acceptance Rate 186 of 424 submissions, 44%

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    • (2022)A Truthful Mechanism for Multibase Station Resource Allocation in Metaverse Digital Twin FrameworkProcesses10.3390/pr1012260110:12(2601)Online publication date: 5-Dec-2022
    • (2022)Classroom Roll Call System Based on Face Detection Technology2022 10th International Conference on Information and Education Technology (ICIET)10.1109/ICIET55102.2022.9779009(42-46)Online publication date: 9-Apr-2022

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