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28 December 2022 YOLOv3 tiny vehicle: a new model for real-time vehicle detection
Xiuxin Ma, Huaiyu Zhuang, Jiaxian Deng, Jia Ren, Yani Cui
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Proceedings Volume 12506, Third International Conference on Computer Science and Communication Technology (ICCSCT 2022); 125061Z (2022) https://doi.org/10.1117/12.2661789
Event: International Conference on Computer Science and Communication Technology (ICCSCT 2022), 2022, Beijing, China
Abstract
Existing vehicle detection has the problem of unbalanced detection accuracy and speed. Aiming at this problem, this paper proposes a new real-time vehicle detection model named YOLOv3 Tiny Vehicle. The proposed network replaces the Maxpooling layers of the original network with the convolutional layers to ensure that the characteristic information of the vehicle was preserved to the greatest extent. On this basis, our work adds a dense connection structure to the original network, which greatly reduces or even eliminates the overfitting problem during network training. The experimental results show that the mean Average Precision (mAP) of the model on the Beijing Institute of Technology vehicle (BIT-Vehicle) dataset can reach 96.80%, the Frames per second (FPS) can reach 188. At the same time, it also shows that our model has preeminent generalization ability.
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Xiuxin Ma, Huaiyu Zhuang, Jiaxian Deng, Jia Ren, and Yani Cui "YOLOv3 tiny vehicle: a new model for real-time vehicle detection", Proc. SPIE 12506, Third International Conference on Computer Science and Communication Technology (ICCSCT 2022), 125061Z (28 December 2022); https://doi.org/10.1117/12.2661789
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KEYWORDS
Detection and tracking algorithms
Performance modeling
Data modeling
Target detection
Environmental sensing
Eye models
Feature extraction
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