XW-improved YOLOv8: Algorithm for Cigarette Small Target Detection

In recent years, fire accidents caused by smoking occur frequently, so it is particularly important to detect cigarettes in non-smoking places. Aiming at the problems that cigarette targets are small, their features are not obvious, and the detection accuracy of existing target detection methods is low, a cigarette small target detection model based on improved YOLOv8 is proposed. Firstly, a small target detection layer is added to the YOLOv8 model, so that the model retains more small target feature information, pays more attention to shallow features, and improves the model’s ability to detect small targets. Finally, the boundary loss function is modified to Wise-IoU v3, and the loss function is optimized through the dynamic non-monotonic mechanism and the gradient gain allocation strategy to optimize the weighted processing of small targets dynamically, so as to improve the model’s detection ability of small targets. The experimental results show that compared to the original model, XW-Improved YOLOv8’s mAP@ 50 increased by 10.60%, and F1 score increased by 13.44%, which can be used for cigarette detection in no-smoking occasions.