Fast Pedestrian Detection Algorithm Based on Improved YOLOv3
Authors: 
Jiahao Li, Yin Tian, Yanxuan Jiang, Jie Yang, Zhichao Chen, Zhichen FengAuthors Info & Claims
Jiahao Li, Yin Tian, Yanxuan Jiang, Jie Yang, Zhichao Chen, Zhichen FengAuthors Info & ClaimsPages 1771 - 1778
Abstract
Aiming at the problems of fast-moving speed, easy occlusion, and complex background of pedestrians in traffic scenes, a fast pedestrian detection algorithm based on improved YOLOv3 is proposed. First, choose the efficient lightweight network ShuffleNetv2 to replace the original backbone network Darknet-53 to reduce the model complexity and improve the detection speed. Second, a reverse residual structure is introduced in the detection network layer to enhance the expressiveness of features. Third, a coordinate attention mechanism is introduced to suppress useless information and enhance the network's ability to focus on key features. Fourth, the spatial pyramid pooling structure is introduced to realize multi-scale feature fusion of the network and improve the detection accuracy of small objects. The experimental results show that compared with YOLOv3, the improved YOLOv3 proposed in this paper can improve the detection accuracy and detection speed by 0.7% and 53.8% respectively, which is more conducive to the rapid detection of pedestrians.
References
[1]
B Wang, C Wu. Using an evidence-based safety approach to develop China's road safety strategies (Article) [J]. Journal of Global Health, 2019, Vol. 9(2): 020602.
[2]
J. Y Wu, X Wang, Y. K Dang, Digital twins and artificial intelligence in transportation infrastructure: Classification, application, and future research directions [J]. Computers and Electrical Engineering, 2022, Vol. 101: 107983.
[3]
Applicability of Computer Vision Architectures and Their Influence on Traffic Safety of Autonomous Vehicles [J]. International Journal of Engineering and Advanced Technology, 2019, 8(6).
[4]
D Tian, Y Han, B Y Wang, A Review of Intelligent Driving Pedestrian Detection Based on Deep Learning [J]. Computational intelligence and neuroscience, 2021, 2021.
[5]
P Viola, M Jones. Rapid object detection using a boosted cascade of simple features [C]//Computer Vision and Pattern Recognition, 2001. CVPR 2001. Proceedings of the 2001 IEEE Computer Society Conference on IEEE, 2001, 1: I.
[6]
N Dalal, B Triggs. Histograms of oriented gradients for human detection [C]//Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on IEEE, 2005, 1: 886-893.
[7]
F Pedro, M David, R Deva. A Discriminatively Trained, Multiscale, Deformable Part Model [C]//2008IEEE Conference on Computer Vision and Pattern Recognition, Anchorage, AK, USA: IEEE, 2008: 1-8.
[8]
D Piotr, A Ron, B Serge, Fast feature pyramids for object detection [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014, 36 (8): 1532-1545.
[9]
A Sarfraz, H. M Nazmul, R Sujan, Pedestrian and Cyclist Detection and Intent Estimation for Autonomous Vehicles: A Survey [J]. Applied Sciences, 2019, Vol. 9(11): 2335.
[10]
S. Q Ren, K. M He, R Girshick, Faster r-cnn: Towards realtime object detection with region proposal networks [C]. Advances in neural information processing systems, 2015: 91-99.
[11]
J Redmon, S Divvala, R Girshick, You only look once: Unified, real-time object detection [C] //Proceedings of the IEEE conference on computer vision and pattern recognition, June 26-July 1, 2016, Las Vegas, NV, USA: IEEE Press, 2016, 9905: 779-788.
[12]
W Liu, D Anguelov, D Erhan, SSD: Single shot multibox detector [C]. European conference on computer vision. Springer, Cham, 2016: 21-37.
[13]
J Redmon, A Farhadi. Yolov3: An incremental improvement[J]. arXiv preprint arXiv: 1804. 02767, 2018.
[14]
R Girshick. Fast R-CNN [C] // IEEE International Conference on Computer Vision. IEEE Computer Society, 2015: 1440-1448.
[15]
Q. B Hou, D. Q Zhou, J. S Feng. Coordinate Attention for Efficient Mobile Network Design [A]. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) [C], 2021.
[16]
P. Y Zhang, Y. X Zhong, X. Q Li. SlimYOLOv3: Narrower, faster and better for real-time UAV applications [J]. arXiv, 2019.
[17]
N. N Ma, X. Y Zhang, H. T Zheng, Shufflenet v2: practical guidelinesfor efficient cnn architecture design [C] //Proceedings of the European Conference on Computer Vision (ECCV), 2018: 116-131.
[18]
M Sandler, A Howard, M. L Zhu, Mobilenet V2: inverted residuals and linear b ottlenecks [C] //The IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Salt Lake: IEEE, 2018: 4510-4520.
[19]
A Bochkovskiy, C. Y Wang, H Liao. YOLOv4: Optimal Speed and Accuracy of Object Detection [C]. CVPR 2020.
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- Fast Pedestrian Detection Algorithm Based on Improved YOLOv3
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Published In
October 2022
1999 pages
ISBN:9781450397148
DOI:10.1145/3573428
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Published: 15 March 2023
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EITCE 2022
EITCE 2022: 2022 6th International Conference on Electronic Information Technology and Computer Engineering
October 21 - 23, 2022
Xiamen, China
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