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基于YOLO的轻量级算法用于行人异常检测,并行交叉融合

Peiyi Jia¹, Hu Sheng¹, Shijie Jia¹

¹School of Rail Intelligent Engineering, Dalian Jiaotong University, Dalian 116028, China.

Sensors (Basel, Switzerland)

|May 14, 2025

概括

这项研究介绍了LPCF-YOLO,一个轻量级的行人异常检测网络. 它显著降低了模型复杂性和计算成本,同时保持或提高了现实应用的检测准确性.

科学领域:

计算机视觉计算机视觉
深度学习 (Deep Learning) 是一种深度学习.
人工智能的人工智能

背景情况:

目前的行人异常检测网络往往太复杂,无法实际部署.
高计算要求阻碍了先进的检测模型在现实世界中应用.

研究的目的:

开发一个轻量级的异常检测网络,以有效监控行人.
为了减少现有模型的参数数量和计算负载,而不会牺牲性能.

主要方法:

拟议的LPCF-YOLO (轻量级并行交融YOLO) 基于YOLOv8n.
在骨干中引入了FPC-F和S-EMCP模块,以及用于降低成本的ADown模块.
在部部署了一种轻量级高水平选特征金字塔网络 (L-HSFPN).
利用了Wise-IoU的损失,以改善本地化和通用化.

主要成果:

与YOLOv8n.相比,参数降低了30.33%,FLOP降低了79.01%,与YOLOv8n.相比,参数减少了30.33%,FLOP降低了79.01%.
实现了2.09M参数和1.7G FLOP,FPS增加了179.62%至43.9.9.
在UCSD-Ped1和UCSD-Ped2数据集上保持或略有改进的平均精度 (mAP@0.5).

结论:

对于行人异常检测,LPCF-YOLO提供了一种更有效的替代方案.
拟议的轻量级架构使异常检测系统能够在现实世界中实际部署.

关键词:

在LPCF-YOLO中使用.轻量级的特征提取轻量级的特征提取通过并行交叉融合.步行者异常检测检测异常检测

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