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International Conference on Artificial Intelligence and Security

ICAIS 2022: Artificial Intelligence and Security pp 51–61Cite as

Anomaly Detection Model for Key Places Based on Improved YOLOv5

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13339))

Abstract

In recent years, key places such as underground stations and train stations, which are crowded and highly mobile, have become key targets for abnormal behaviour such as violence by some extremists or violent elements. The public safety risks in key places cannot be ignored, and the need to detect abnormal behaviour in key places is urgent in order to protect the personal safety of the people in such key places. When abnormal people and abnormal events occur in key places, timely detection and early warning are required to prevent and protect the safety of the people in a timely manner. Therefore, a real-time anomaly detection system based on the improved YOLOv5 key place video is proposed for such key places with dense personnel, intricate and complex identities, low accuracy of anomalous behaviour detection and slow detection speed. The method improves the target recognition effect by improving the loss function and optimising the resolution. Test results show that under the same training conditions, the improved YOLOv5 network has a significantly higher correct rate of anomalous behaviour detection and a faster detection speed of anomalous behaviour compared with the original YOLOv5 network.

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Acknowledgement

This work was supported by the Open Research Fund of the Public Security Behavioral Science Laboratory, People’s Public Security University of China [Grants 2020SYS03], the Fundamental Research Funds for the Central Universities, People’s Public Security University of China (2021JKF215) and the Fund for the training of top innovative talents to support master’s degree program, People’s Public Security University of China (2021yjsky018).

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Authors and Affiliations

  1. Department of Police Information Engineering and Cyber Security, People’s Public Security University of China, Beijing, 100038, China

    Wang Yuanxin, Yuan Deyu & Yuyan Meng

  2. Key Laboratory of Safety Precautions and Risk Assessment, Ministry of Public Security, Beijing, 102623, China

    Yuan Deyu

  3. Public Security Behavioral Science Laboratory, People’s Public Security University of China, Beijing, 102623, China

    Ding Meng

Authors
  1. Wang Yuanxin
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  2. Yuan Deyu
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  3. Yuyan Meng
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  4. Ding Meng
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Corresponding author

Correspondence to Yuan Deyu .

Editor information

Editors and Affiliations

  1. Nanjing University of Information Science and Technology, Nanjing, China

    Xingming Sun

  2. Nanjing University of Information Science and Technology, Nanjing, China

    Xiaorui Zhang

  3. Jinan University, Guangzhou, China

    Zhihua Xia

  4. Purdue University, West Lafayette, IN, USA

    Elisa Bertino

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Yuanxin, W., Deyu, Y., Meng, Y., Meng, D. (2022). Anomaly Detection Model for Key Places Based on Improved YOLOv5. In: Sun, X., Zhang, X., Xia, Z., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2022. Lecture Notes in Computer Science, vol 13339. Springer, Cham. https://doi.org/10.1007/978-3-031-06788-4_5

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  • DOI: https://doi.org/10.1007/978-3-031-06788-4_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-06787-7

  • Online ISBN: 978-3-031-06788-4

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