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An Improved Lightweight YOLOv5-Based Face Target Detection Algorithm

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Proceedings of the 2nd International Conference on Internet of Things, Communication and Intelligent Technology (IoTCIT 2023)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 1197))

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Abstract

In this paper, an improved lightweight YOLOv5 based on a face target detection algorithm is proposed. First, build a small data set on top of the public data set. Next, the anchor frame is reset and the data annotation is standardized they improve the quality of the data set and improve the accuracy and speed of the model. The simulation results showed that the accuracy and mAP value of the improved YOLOv5 network reached 95.52% and 95.15%, respectively. They both have a 15% increase, and the computing speed has also increased by 19.00%. At last, this paper realizes faster and more accurate detection than the traditional YOLOv5 network.

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Author information

Authors and Affiliations

  1. School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai, 201620, China

    Tianlin Hui & Li Zhao

  2. Anhui Zhiguo Intelligent Technology Co., Ltd., Chuzhou, 239000, Anhui, China

    Li Zhao

Authors
  1. Tianlin Hui
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  2. Li Zhao
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Corresponding author

Correspondence to Li Zhao .

Editor information

Editors and Affiliations

  1. Central South University, Changsha, Hunan, China

    Jian Dong

  2. School of Information Engineering, Shenzhen University, Shenzhen, Guangdong, China

    Long Zhang

  3. School of Information and Control Engineering, China University of Mining and Technolog, Xuzhou, Jiangsu, China

    Deqiang Cheng

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Cite this paper

Hui, T., Zhao, L. (2024). An Improved Lightweight YOLOv5-Based Face Target Detection Algorithm. In: Dong, J., Zhang, L., Cheng, D. (eds) Proceedings of the 2nd International Conference on Internet of Things, Communication and Intelligent Technology. IoTCIT 2023. Lecture Notes in Electrical Engineering, vol 1197. Springer, Singapore. https://doi.org/10.1007/978-981-97-2757-5_44

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  • DOI: https://doi.org/10.1007/978-981-97-2757-5_44

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-97-2756-8

  • Online ISBN: 978-981-97-2757-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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