Issue |
MATEC Web Conf.
Volume 355, 2022
2021 International Conference on Physics, Computing and Mathematical (ICPCM2021)
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Article Number | 03020 | |
Number of page(s) | 11 | |
Section | Computing Methods and Computer Application | |
DOI | https://doi.org/10.1051/matecconf/202235503020 | |
Published online | 12 January 2022 |
A pedestrian detection algorithm for low light and dense crowd Based on improved YOLO algorithm
China University of Mining and Technology-Beijing, Beijing, China
* Corresponding author: 1810480225@student.cumtb.edu.cn
The real-time pedestrian detection algorithm requires the model to be lightweight and robust. At the same time, the pedestrian object detection problem has the characteristics of aerial view Angle shooting, object overlap and weak light, etc. In order to design a more robust real-time detection model in weak light and crowded scene, this paper based on YOLO, raised a more efficient convolutional network. The experimental results show that, compared with YOLOX Network, the improved YOLO Network has a better detection effect in the lack of light scene and dense crowd scene, has a 5.0% advantage over YOLOX-s for pedestrians AP index, and has a 44.2% advantage over YOLOX-s for fps index.
Key words: Weak light detection / Crowd detection / YOLO Network / Pedestrian detection
© The Authors, published by EDP Sciences, 2022
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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