电子科技2026，Vol.39Issue(10)：34-41,8.DOI:10.16180/j.cnki.issn1007-7820.2026.10.004
基于复杂环境的人员跌倒检测算法
Pedestrian Fall Detection Algorithm Based on Complex Environments
摘要
Abstract
In view fo the problems such as target occlusion and small targets in complex environments,an im-proved personnel fall detection algorithm based on the YOLOv7(You Only Look Once version 7)model is proposed.The improved model introduces a new module,SPINeXt,which combines SPDConv(Space-to-Depth Convolution)with InceptionNeXt,to fully extract feature information and thereby enhance the fall detection performance in oc-clented environments.The normalized Wasserstein distance loss was proportionally added to the original CIoU(Com-plete Intersection over Union),enhancing the sensitivity of the original YOLOv7 model to the position deviation of small targets.The Mosaic and Mixup data augmentation techniques are adopted to further enhance the robustness of the original YOLOv7 model,thereby obtaining more accurate detection results.The experimental results show that the accuracy rate of fall detection of the improved model in complex environments is 0.956,which is superior to oth-er network models,verifying that the proposed model has the best comprehensive performance in personnel fall de-tection in complex environments.关键词
跌倒检测/深度学习/图像处理/神经网络/YOLOv7/损失函数/数据增强/目标检测Key words
fall detection/deep learning/image processing/neural network/YOLOv7/loss function/data enhancement/target detection分类
信息技术与安全科学引用本文复制引用
王菲,张运楚,张汉元..基于复杂环境的人员跌倒检测算法[J].电子科技,2026,39(10):34-41,8.基金项目
国家自然科学基金(62003191) National Natural Science Foundation of China(62003191) （62003191）
