Elderly Fall Detection Based on Improved YOLOv5s Network

Abstract

The problem of aging population in our country is becoming more and more serious, falling on the road accidently has been the first murder for people over 65 years of age. In this article, a real-time detection method for elderly fall behavior based on improved YOLOv5s is proposed to detect whether the elderly fall in real time, so that they can receive timely and effective treatment. First, the asymmetric convolution blocks (ACB) convolution module is used in the Backbone network to replace the existing basic convolution to improve the feature extraction capability. Then, the spatial attention mechanism module is added to the residual structure of the Backbone network to extract more feature location information. Finally, the feature layer structure is improved to remove the feature layer for small targets so that the network can pay more attention to the semantic level information, and at the same time, the classifier is set. The proposed algorithm is trained on the URFD public dataset, and the test set is used for verification. The experimental results show that the average accuracy of all categories of the algorithm reaches 97.2%, which is increased by 3.5% compared to YOLOv5s. Thus the proposed algorithm can accurately detect the fall behavior of the elderly.