Design and implementation of indoor smokers detection and alarm system based YOLOv5

In order to solve the automatic detection and alarm of indoor smokers, this paper designs and implements an indoor smoker detection and alarm system based on YOLOv5 model. This paper uses YOLOv5 as the target recognition model to complete the recognition of cigarettes and human objects, uses Deepsort target tracking algorithm to achieve the determination of smoking action, and combines Facenet face recognition algorithm and sklearn framework and other technologies to achieve face recognition and alarm function. The test experiment proves that the accuracy rate of the smoking person detection and alarm system based on the method of this paper is high and has certain practical and promotion prospects.