Design of a human fall detection algorithm based on deep learning
An improved YOLOV7 algorithm is proposed to address the inaccurate and insufficient effectiveness of traditional de-tection algorithms in detecting human falls.Inserting Plug-and-play transformer module into YOLOV7 backbone network and detection head network.Replacing the 3*3 convolution in the efficient aggregation network in YOLOV7 improves the detection accuracy of the model while ensuring detection speed,with an average accuracy improvement of 12.32%,an accuracy improvement of 5.01%,and a re-call improvement of 3.33%.The improved YOLOV7 human fall detection model can meet the deployment and efficient detection of var-ious devices in different application scenarios.
国家科技期刊平台
NSTL
万方数据
维普
