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FedSH: a federated learning framework for safety helmet wearing detection

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Abstract

Safety helmet wearing detection based on video surveillance is an important means of safety monitoring in many industrial scenes. The training of safety helmet wearing detection models requires large and well-labeled dataset. However, the incidence of security violations is relatively low, which results in insufficient samples for training deep detection models. Safety helmet wearing detection is a common requirement in many scenarios such as construction sites, substations, and factory workshops. Aggregating data from multiple companies for model training would improve the performance of the detection model. Traditional centralized training methods are not feasible because aggregating data in centralized locations (such as the cloud) can raise concerns about data privacy and the high cost of data communication and storage. This paper proposes FedSH, a novel cloud-edge-based federated learning framework, which learns a shared global safety helmet wearing detection model in the cloud from multiple companies at the network edges and achieves data privacy protection by keeping company data locally. In addition, this paper designs reweighting mechanisms and applies transfer learning to address class imbalance and non-IID problems in the training data, so as to obtain an accurate and personalized detection model. Extensive experiments have been conducted on real surveillance video datasets. The experimental results demonstrate that FedSH outperforms the existing widely used federated learning methods with an accuracy improvement of at least 3.4%; the reduction in accuracy is within the range of 5% compared with centralized learning methods. FedSH effectively achieves a good balance between model performance, privacy protection, and communication efficiency.

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Data availability statement

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Authors and Affiliations

  1. Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China

    Zhiqing Huang & Xiao Zhang

  2. School of Automation and Intelligence, Beijing Jiaotong University, Beijing, 100044, China

    Yanxin Zhang

  3. School of Electronic and Information Engineering, Tongji University, Shanghai, 201804, China

    Yusen Zhang

Authors
  1. Zhiqing Huang
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  2. Xiao Zhang
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  3. Yanxin Zhang
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  4. Yusen Zhang
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Contributions

XZ and ZH conceived of and designed the experiments. XZ performed the experiments and analyzed the data. YZ collected the dataset used in this study. ZH, XZ, YZ, YZ wrote this paper together.

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Correspondence to Zhiqing Huang.

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Huang, Z., Zhang, X., Zhang, Y. et al. FedSH: a federated learning framework for safety helmet wearing detection. Neural Comput & Applic 36, 10699–10712 (2024). https://doi.org/10.1007/s00521-024-09632-y

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  • DOI: https://doi.org/10.1007/s00521-024-09632-y

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