Smoking Detection Model Based on Improved YOLOv8-s

Target detection algorithms face challenges in smoking detection tasks, particularly in the identification of small targets and the occurrence of misidentification in various scenes. In this paper, we propose Smoking-YOLO based on YOLOv8-s, which adopt ConvNeXtv2 as the backbone that can extract features at four scales, obtaining stronger contextual information to enhance small target detection capability. During the feature fusion stage, we employ a bidirectional three-channel four-scale fusion strategy in the fusion stage to output four-scale prediction maps, strengthening the semantic information focus on smoking details and improving the ability to distinguish pseudo-smoking behaviors. Finally, we adds a slide weighting function to enhance attention to hard negative samples. Experimental results on the self-built Smoking-3k dataset show that our model achieves a detection effect of AP_small 0.31 for small targets, an improvement of 10.6 . The model's precision and recall reach mAP_ 0.5 : 0.947 and mAP_ 0,5:0.95 :0.652, respectively, increasing by 3.1 and 7 , demonstrating the effectiveness of the model improvement. The code is available at https: github.com TaroPlay Smoking-YOLO.git