Lightweight deep learning model for crime pattern recognition based on transformer with simulated annealing sparsity and CNN
Year: 2025
Type: article
Abstract: 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.
Source: Scientific Reports
Authors: HongYuan Lu, ChengXin Chen, YuQi Ma, YanMing Ma
Institution: China People's Public Security University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,690
OpenAlex ID: W4414428336