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密集流的YOLOv8n:一种轻量级的框架,用于智能图书馆的实时人群监控
Zini Chen1, Xuming Xie2, Taorong Qiu3
1School of Mathematics and Computer Sciences, Nanchang University, Nanchang, 330000, China.
Scientific reports
|April 4, 2025
概括
本研究介绍了一种优化的YOLOv8n模型,用于智能图书馆的实时人群监控. 改进后的模型实现了高精度,并大大提高了处理速度,以实现高效的行人流量分析.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 智能系统 智能系统
背景情况:
- 现有的人群监控模型在密集,封闭的图书馆环境中缺乏实时性能和准确性.
- 有效的群众监控对于优化资源和改善智能图书馆服务至关重要.
研究的目的:
- 为智能图书馆开发一种轻量级,准确和实时的人群监控解决方案.
- 解决高密度和封闭环境的现有模型的局限性.
主要方法:
- 从图书馆环境中收集并注释了5350张行人流动图像的数据集.
- 设计了一个轻量级的卷积数据增强模块 (DensityNet).
- 应用模型修剪和知识蒸到YOLOv8n模型中,包含一个区域检测算法.
主要成果:
- 改进的YOLOv8n模型实现了0.99的mAP@0.5和0.861.95的mAP@0.5:0.95.
- 实时性能显著改善,率 (FPS) 为254.
- 计算负载降至4.0 GFLOP,参数数量为2.04M.
结论:
- 开发的模型为智能图书馆中的人群流量监控提供了准确且无接触的解决方案.
- 优化的模型满足了对高峰行人流量的实时检测需求.
- 集成到智能图书馆管理系统可以提高效率和资源优化.
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