现代信息科技2025，Vol.9Issue(5)：66-71,6.DOI:10.19850/j.cnki.2096-4706.2025.05.012
基于改进YOLOv8的复杂场景跌倒检测算法
Fall Detection Algorithm in Complex Scenes Based on Improved YOLOv8
曹萌迪 1杨梦凡 2王留毅 1王东洋1
作者信息
- 1. 华北水利水电大学,河南 郑州 450046
- 2. 河南水利与环境职业学院,河南 郑州 450008
- 折叠
摘要
Abstract
Aiming at the problems of low fall detection accuracy and poor real-time performance due to factors such as lighting changes,dense population,and occluded human form,this paper proposes a fall detection algorithm in complex scenes based on YOLOv8 of SLG-YOLO.The SC module is designed and embedded in the C2f module to extract edge features of fall behavior using edge detection algorithms,and the Large Separable Kernel Attention(LSKA)is introduced in the SPPF module,which utilizes large separable convolution to capture a wide range of contextual information of images,enhancing the attention to important features without increasing the computational complexity.At the same time,it adopts the Gather and Distribute(GD)mechanism,and combines with the Multi-scale Sequence Feature Fusion method(SSFF)of 3D convolution to replace the original Neck part,which improves the detection accuracy of the model for complex scenes and targets of different scales.The experimental results show that the SLG-YOLO fall detection algorithm improves the precision by 2.2%,recall by 3.0%,mAP@0.5 by 3.6%,and mAP@0.5:0.95 by 3.1%based on the number of parameters and calculated quantities of 6.2 M and 10.7 FLOPs.关键词
跌倒检测/边缘检测/注意力机制/YOLOv8Key words
fall detection/edge detection/Attention Mechanism/YOLOv8分类
信息技术与安全科学引用本文复制引用
曹萌迪,杨梦凡,王留毅,王东洋..基于改进YOLOv8的复杂场景跌倒检测算法[J].现代信息科技,2025,9(5):66-71,6.
