Human fall detection algorithm based on improved YOLOv5
Due to the rapid change of human movement,many posture and complex environment,the fall detection algorithm will have the problems of false detection,missing detection and slow detection speed.To solve the above problems,an improved YOLOv5 algorithm is proposed for real-time fall detection in home environment.RepVGG is used to optimize the Backbone of YOLOv5 to enhance the feature extraction capability of the backbone network.The convolutional attention mechanism is added to the network to strengthen the model's attention to important features.The weighted bidirectional feature pyramid network is introduced into the feature fusion part and simplified to integrate features of different scales more fully.The experimental results show that the accuracy,recall rate and average accuracy of the method are improved by 3.43%,1.41%and 3.1%respectively,which optimizes the detection effect and better meets the practical requirements.
万方数据
维普
