Research on smoking behavior recognition algorithms based on improved YOLOv7
Aims:A smoking behavior recognition method based on improved YOLOv7 was proposed to improve the efficiency and accuracy of artificial intelligence in smoking behavior recognition.Methods:Based on the YOLOv7 algorithm,the GhostNet network structure was used to replace its backbone network,reducing the number of network model parameters and computational complexity.The CBAM attention mechanism was introduced to improve the effectiveness of feature extraction.A multi-scale feature fusion module and an improved loss function were incorporated to enhance the model's detection performance in complex environments.Results:Testing conducted on a smoking dataset showed that the improved model reduced the number of parameters and computational complexity by 16.6% and 37.4% ,respectively.The detection speed was improved to 103.4 F/s;and the accuracy was improved by 2.8% .Conclusions:The proposed lightweight network model can meet the requirements of real-time video monitoring and can achieve real-time detection on low-power embedded devices.
smoking behaviorYOLOv7lightweight networkattention mechanismmulti-scale feature fusionloss function
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