Smoking Detection Algorithm Based on Improved YOLOv5

Abstract

Aiming at the current problems in smoking detection in public places, such as many small and medium-sized cigarette targets, low pixels occupied by cigarette targets, indistinct characteristics of cigarette targets, and difficult detection, an improved smoking detection algorithm YOLOv5_EC is proposed. Based on the YOLOv5, this paper redesigns the neck structure and proposes the EFFN (Enhanced Feature Fusion Neck) structure, which fuses three adjacent feature maps of different sizes to better retain the positioning information of the target, further improves the feature expression ability of small objects, and then introduce the CBAM attention module in the network, so that the model can focus on the important areas of the image, suppress the interference of irrelevant information, enhances the model's ability to learn features, and improves the accuracy of model detection. Experiments show that the mAP@0.5 of the model proposed in this paper is improved by 1.5% compared with the original YOLOv5 model, and the improved model can effectively identify smoking behavior in actual scenes.