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Simulation Tools and Techniques. 13th EAI International Conference, SIMUtools 2021, Virtual Event, November 5-6, 2021, Proceedings

Research Article

Optimization of Loss Function for Pedestrian Detection

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  • @INPROCEEDINGS{10.1007/978-3-030-97124-3_39,
        author={Shuo Zhang and Kailiang Zhang and Yuan An and Shuo Li and Yong Sun and Weiwei Liu and Likai Wang},
        title={Optimization of Loss Function for Pedestrian Detection},
        proceedings={Simulation Tools and Techniques. 13th EAI International Conference, SIMUtools 2021, Virtual Event, November 5-6, 2021, Proceedings},
        proceedings_a={SIMUTOOLS},
        year={2022},
        month={3},
        keywords={Computer vision Deep learning Pedestrian detection Loss function},
        doi={10.1007/978-3-030-97124-3_39}
    }
    
  • Shuo Zhang
    Kailiang Zhang
    Yuan An
    Shuo Li
    Yong Sun
    Weiwei Liu
    Likai Wang
    Year: 2022
    Optimization of Loss Function for Pedestrian Detection
    SIMUTOOLS
    Springer
    DOI: 10.1007/978-3-030-97124-3_39
Shuo Zhang1, Kailiang Zhang1,*, Yuan An1, Shuo Li1, Yong Sun1, Weiwei Liu2, Likai Wang2
  • 1: Jiangsu Province Key Laboratory of Intelligent Industry Control Technology, Xuzhou University of Technology
  • 2: Traffic Police Detachment, Xuzhou Police Bureau
*Contact email: zhangkailiang@xzit.edu.cn

Abstract

The advanced intelligent driving assistance system has improved the current traffic congestion to a great extent and effectively reduced frequent traffic safety accidents. Pedestrian detection technology is the core of autonomous driving technology, and its accuracy, real-time and complexity will directly determine the safe operation of autonomous driving. In the case of heavy traffic, detecting a single pedestrian in a crowd is still a challenging problem. Considering the problem of mutual occlusion between pedestrians in dense crowds, an improved function algorithm based on YOLOv3 is proposed to optimize the loss function and increase the accuracy of detection by replacing the anchor frame. Experimental results show that this method can effectively reduce the missed detection rate, increase the average accuracy, and help improve the effectiveness of pedestrian occlusion detection, ensure accurate pedestrian detection in traffic congestion scenarios, and ensure driving safety.

Keywords
Computer vision Deep learning Pedestrian detection Loss function
Published
2022-03-31
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-030-97124-3_39
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