Improved Fall Detection Algorithm Based on YOLOv8: OEF-YOLO

Existing object detection algorithms suffer from low detection accuracy and poor real-time performance when detecting fall events in indoor scenes, owing to changes in angle and light. In response to this challenge, this study proposes an improved fall detection algorithm based on YOLOv8, called OEF...

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Bibliographic Details
Published in:Ji suan ji gong cheng Vol. 51; no. 7; pp. 127 - 139
Main Author: SONG Jie, XU Huiying, ZHU Xinzhong, HUANG Xiao, CHEN Chen, WANG Zeyu
Format: Journal Article
Language:Chinese
English
Published: Editorial Office of Computer Engineering 01.07.2025
Subjects:
object detection|lightweight|falling incidents|attention mechanism|omni-dimensional dynamic convolution(odconv)
ISSN:1000-3428
Online Access:Get full text
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Summary:Existing object detection algorithms suffer from low detection accuracy and poor real-time performance when detecting fall events in indoor scenes, owing to changes in angle and light. In response to this challenge, this study proposes an improved fall detection algorithm based on YOLOv8, called OEF-YOLO. The C2f module in YOLOv8 is improved by using a Omni-dimensional Dynamic Convolution (ODConv) module, optimizing the four dimensions of the kernel space to enhance feature extraction capabilities and effectively reduce computational burden. Simultaneously, to capture finer grained features, the Efficient Multi-scale Attention (EMA) module is introduced into the neck network to further aggregate pixel-level features and improve the network's processing ability in fall scenes. Integrating the Focal Loss idea into the Complete Intersection over Union (CIoU) loss function allows the model to pay more attention to difficult-to-classify samples and optimize overall model performance. Experimental results show that compared to YOLOv8n, OEF-YOLO achieves improvements of 1.5 and 1.4 percentage points in terms of mAP@0.5 and mAP@0.5∶0.95, the parameters and computational complexity are 3.1×106 and 6.5 GFLOPs. Frames Per Second (FPS) increases by 44 on a Graphic Processing Unit (GPU), achieving high-precision detection of fall events while also meeting deployment requirements in low computing scenarios.
ISSN:1000-3428
DOI:10.19678/j.issn.1000-3428.0069257