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<p>This study addresses the pressing need for high efficiency and low resource consumption in crime pattern recognition within public safety governance by proposing a lightweight deep learning model known as the lightweight crime recognition network (LCRNet). Designed to provide intelligent support for forecasting and case classification, LCRNet integrates a Transformer encoder and convolutional neural network. To optimize performance, the model introduces simulated annealing sparsity (SAS) into the multi-head self-attention of the Transformer architecture, thus effectively reducing computational overhead while maintaining accuracy. Experimental results indicate that LCRNet achieves an accuracy of 97.76% on real-world crime data from Los Angeles and demonstrates strong generalizability in cross-dataset testing. Additionally, ablation studies and visualizations of the sparsity process confirm the effectiveness of SAS. This research provides a practical solution for efficient crime pattern recognition and edge device deployment in public safety, and our future work will focus on enhancing model interpretability and validating adaptability in resource-constrained environments.</p>

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Lightweight deep learning model for crime pattern recognition based on transformer with simulated annealing sparsity and CNN

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年9月22日

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  • HongYuan Lu,
  • ChengXin Chen,
  • YuQi Ma,
  • YanMing Ma

摘要

This study addresses the pressing need for high efficiency and low resource consumption in crime pattern recognition within public safety governance by proposing a lightweight deep learning model known as the lightweight crime recognition network (LCRNet). Designed to provide intelligent support for forecasting and case classification, LCRNet integrates a Transformer encoder and convolutional neural network. To optimize performance, the model introduces simulated annealing sparsity (SAS) into the multi-head self-attention of the Transformer architecture, thus effectively reducing computational overhead while maintaining accuracy. Experimental results indicate that LCRNet achieves an accuracy of 97.76% on real-world crime data from Los Angeles and demonstrates strong generalizability in cross-dataset testing. Additionally, ablation studies and visualizations of the sparsity process confirm the effectiveness of SAS. This research provides a practical solution for efficient crime pattern recognition and edge device deployment in public safety, and our future work will focus on enhancing model interpretability and validating adaptability in resource-constrained environments.