改进YOLOV7的跌倒人员检测

doi:10.11896/jsjkx.230800039

Abstract

Abstract: 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 processed,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 improving 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 algorithm and better meets the requirements of fall detection.

Key words: Fall detection, YOLOV7, Jump connection, CBAM, Weighted joins

CLC Number:

TP391

ZHAO Junjie, ZHOU Xiaojing, LI Jiaxing. Improved YOLOV7 for Fall Detection[J].Computer Science, 2024, 51(6A): 230800039-6.

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