Smoking Behavior Detection Based On Improved YOLOv5s Algorithm
Junlong Tang, Shenbo Liu, Bin Zheng, Jun Zhang, Bin Wang, Mukun Yang
Abstract
Smoking behavior in public places and fire bans seriously threatens the safety of people's lives and property. In order to ensure public safety, this paper proposes a smoking behavior detection method based on YOLOv5 algorithm and image processing. Aiming at the small target of cigarettes, this paper uses the K-means algorithm and the method of adding a small target detection layer to improve the YOLOv5 algorithm, and realizes the improvement of the detection accuracy. On the self-made data set, the false detection rate is 0%, and the AP is 92.3%, which is 6.7% higher than that of the YOLOv5s algorithm.
Topics & Concepts
Computer scienceSet (abstract data type)Image (mathematics)Order (exchange)AlgorithmProperty (philosophy)Algorithm designLayer (electronics)Artificial intelligenceData miningOrganic chemistryEconomicsFinancePhilosophyChemistryEpistemologyProgramming languageFire Detection and Safety SystemsVideo Surveillance and Tracking MethodsAir Quality Monitoring and Forecasting