With the advent of the aging population,it is increasingly important for the elderly to be detected and treated in time after falling.In order to improve the detection accuracy and speed of the original YOLOV7,a series of improvements are made to YOLOV7,and a new YOLOV7 structure,namely YOLOV7-CMJ structure,is proposed.Firstly,the collected pictures are pro-cessed,and some pictures are preprocessed with rotation,brightness and other preprocessing,and calibrated to obtain sample datasets.Secondly,the CBAM attention mechanism is introduced to enhance channel attention and spatial attention,thereby im-proving the accuracy of the model.Finally,the original PANet feature fusion in YOLOV7 is changed to MJPANet,that is,multi-beating sign fusion structure,and the previous Concat is replaced by weighting,so as to improve the YOLOV7-CMJ structure.By comparing with the original YOLOV7,it can be seen that the accuracy of the improved algorithm is increased by 7.4％,the recall rate is increased by 7.1％,and the average accuracy is increased by 7.1％,which proves the effectiveness of the improved algo-rithm and better meets the requirements of fall detection.
Fall detectionYOLOV7Jump connectionCBAMWeighted joins
NETL
NSTL
万方数据
