一个基于YOLOv8n的轻量级人体探测器用于监控录像
Qicheng Wang1, Guoqiang Feng1, Zongzhe Li1
1Equipment Management and UAV Engineering School, Air Force Engineering University, Xi'an 710051, China.
Sensors (Basel, Switzerland)
|January 25, 2025
概括
基于YOLOv8n的新型轻量级人体检测模型,可在边缘设备上提供高效的实时监控. 这种优化的模型大大降低了参数和计算成本,同时提高了检测准确性.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 嵌入式系统 嵌入式系统
背景情况:
- 在资源有限的边缘设备上部署人体检测需要具有低计算成本和参数大小的模型.
- 实时性能对于监控应用程序至关重要,要求高效的算法.
研究的目的:
- 开发基于YOLOv8n的轻量级人体检测模型,以便在边缘设备上有效部署.
- 为了平衡高精度,降低计算成本和参数大小,实现实时监控.
主要方法:
- 在YOLOv8n架构中引入并修改了MSBlock模块.
- 开发了一个异质的路径聚合特征金字塔网络 (PAFPN),结合了增强的MSBlock和异质卷积内核.
- 应用注意引导内核转换 (AKConv) 以进一步优化参数和计算负载.
主要成果:
- 与原来的YOLOv8n相比,实现了~10%的参数大小减少和~5%的浮点计算成本下降.
- 在定制监控数据集上,平均平均精度 (mAP@0.5:0.95) 提高了 1.4%.
- 在PASCAL VOC数据集的一个具有挑战性的子集上显示了mAP@0.5的2.8%改善和mAP@0.5:0.95的1.2%改善.
结论:
- 拟议的轻量级模型有效地提高了人体检测的准确性,同时大大降低了计算要求.
- 该模型具有强大的概括能力,适用于边缘设备上的各种监控和计算机视觉任务.
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