## 1 Introduction  
Target detection is a crucial aspect of computer vision, focusing on identifying the location and classification of specific objects within videos or images. With the continuous advancements in computer hardware and various intelligent algorithms, target detection has found widespread applications in fields such as smart factories [1], facial recognition [2], agricultural monitoring [3], and medical analysis [4]. As urbanization accelerates, uncivilized smoking behavior remains prevalent in public spaces, posing significant risks to public health and safety. This highlights the need for effective cigarette detection as a foundation for in-depth research on small target detection technologies. Traditional manual inspection methods are often inadequate due to limitations in manpower and resources, making smoking detection technology essential for managing public areas. Currently, smoking-detection technologies can be categorized into four main types: smoke-feature-based detection [5], wearable-device-based detection [6], sensor-network-based detection [7], and computer vision detection [8]. Smoke-feature-based detection and sensor network methods rely on traditional sensors but face challenges such as environmental interference and false detections. Wearable devices assess smoking behavior by recognizing hand movements; however, they tend to be costly and inconvenient to use. In contrast, computer vision techniques offer greater potential by directly identifying cigarettes through image processing. The evolution of the YOLO series [9; 10; 11; 12; 13; 14; 15; 16; 17; 18] has significantly advanced object detection capabilities. YOLOv8, as one of the more refined versions, has improved detection accuracy and real-time performance through its lightweight design and optimized training strategies. [PERSON] et al. [19] proposed an enhanced YOLOv8 neural network model based on deep learning, which incorporates an improved ShapeIoU (Shape Intersection over Union) and receptive field attention convolution for the efficient and precise detection of foreign objects in Pu'er sun-dried green tea. [PERSON] et al. [20] proposed a YOLO-SGF lightweight network for detecting complex infrared images. This model employs GSConv (Global Spatial Convolution) and convolution to process deep and shallow features, respectively, while combining ShuffleNetV2-block1 with C2f for infrared feature extraction. Additionally, FIMPDIoU (Focal Mean Pairwise Distance Intersection over Union) is used to focus on objects that are easily overlooked in infrared imagery. [PERSON] et al. [21] developed a YOLOv8-ECFS lightweight algorithm for weed species detection in soybean fields. This model integrates a coordinate attention module and EfficientNet (Efficient Convolutional Neural Network) and optimizes the regression accuracy of bounding boxes using Focal_SIoU, ensuring precise identification of various weed species. [PERSON] et al. [22] introduced the LS-YOLOv8s model for detecting and grading the maturity of strawberries. This model, based on the YOLOv8s algorithm, incorporates the Lightweight Swin Transformer module and two new random variables to control the effects of data augmentation. [PERSON] et al. [23] introduced an algorithm called CoT-YOLOv8 to enhance small target detection in aerial images. This model integrates the Convolutional Block Attention Module (CBAM) and the dynamic convolution module, utilizing a context transformer to assist in target detection. [PERSON] et al. [24] expanded on YOLOv8 by incorporating the Attention Module convolution and the Context Information Enhancement Module (CIEM), achieving impressive results in detecting foreign objects on railway tracks. [PERSON] et al. [25] developed the CDD-YOLOv8 algorithm for identifying defects on cigarette packaging, which features a small target detection head and the convolutional attention module CBAM, significantly improving the model's capability to locate and identify small defects while minimizing both missed and false detections.