计算机工程与科学2024，Vol.46Issue(8)：1455-1465,11.DOI:10.3969/j.issn.1007-130X.2024.08.014
基于YOLOv8改进的室内行人跌倒检测算法FDW-YOLO
FDW-YOLO:An improved indoor pedestrian fall detection algorithm based on YOLOv8
摘要
Abstract
Aiming at the problem of low fall detection accuracy and poor real-time performance in in-door scenes due to the effects of light change,occlusion of the human body form,and changes in the hu-man body posture under special viewpoint,a lightweight improved fall detection algorithm based on YOLOv8,named FDW-YOLO,is proposed.The C2f module in the backbone network is replaced by the FasterNext module,which reduces the computational complexity while retaining the excellent fea-ture extraction capability.According to the characteristics of human falls with large changes in posture,three network structures with dynamically deformable convolutional modules added at different positions in the neck layer are designed,experiments are conducted on a self-made fall dataset for comparison,and ultimately,the YOLOv8-C scheme is selected based on network performance.A bounding box re-gression loss function WIoU is introduced into the improved network to replace the original CIoU.The experimental results show that compared with YOLOv8n,the FDW-YOLO fall detection algorithm in-creases mAP@0.5 from 96.5％to 97.9％and mAP@0.5:0.95 from 72.5％to 74.3％,while the num-ber of parameters and computation is only 4.1 × 106 and 7.3 × 109,which is in line with the requirements for deployment in low-computing power industrial scenarios.关键词
目标检测/跌倒/FasterNext/DDConv/WIoUKey words
object detection/fall/FasterNext/DDConv/WIoU分类
信息技术与安全科学引用本文复制引用
陈晨,徐慧英,朱信忠,黄晓,宋杰,曹雨淇,周思瑜,盛轲..基于YOLOv8改进的室内行人跌倒检测算法FDW-YOLO[J].计算机工程与科学,2024,46(8):1455-1465,11.基金项目
国家自然科学基金(62376252,61976196) （62376252,61976196）
浙江省自然科学基金重点项目(LZ22F030003) （LZ22F030003）
国家级大学生创新创业训练计划项目创新训练重点项目(202310345042) （202310345042）
