## 1 Introduction
However, cigarette detection still poses several challenges, such as interference in complex environments, the precision of small object detection, insufficient datasets, and hardware limitations. [PERSON] et al. [26] addressed the overlooked challenge of small object detection by creating a specialized benchmark dataset and enhancing the R-CNN algorithm with a context model and small region proposal generator, achieving a 29.8% improvement in mean average precision over the original R-CNN. [PERSON] et al. [27] enhanced Faster R-CNN for small object detection by introducing an improved IoU-based loss function, bilinear-interpolated RoI pooling, multi-scale feature fusion, and refined NMS, achieving 90% recall and 87% accuracy on low-resolution traffic signs, significantly outperforming the original model. [PERSON] et al. [28] proposed a context-aware object detection method with multi-scale feature fusion and attention mechanisms to address the challenges of detecting low-resolution small objects, achieving 78.1% mAP on the PASCAL VOC2007 dataset, outperforming traditional SSD models. [PERSON] et al. [29] enhanced YOLOv5 through structural modifications to develop YOLO-Z models, achieving a 6.9% mAP improvement for small object detection at 50% IoU with only a 3 ms increase in inference time, demon -strating optimized performance for autonomous racing applications and informing future detector adaptations in autonomous systems. [PERSON] et al. [30] proposed DNTR, a novel framework combining DeNoising Feature Pyramid Network (DN-FPN) with Transformer-based Trans R-CNN for tiny object detection in geoscience, employing contrastive learning for feature denoising and self-attention mechanisms, achieving 17.4% AP\({}_{\text{vt}}\) improvement on AI-TOD and 9.6% AP gain on VisDrone datasets compared to baselines. In this paper, we propose a lightweight small object detection algorithm based on an improved version of the YOLOv8 algorithm. Our method enhances the YOLOv8 architecture by introducing a self-created light-weighting module and a new loss function, which emphasizes model efficiency while effectively capturing information from small targets. We refer to our improved algorithm as LSOD-YOLOv8. Specifically, we implement a lightweight adaptive weight downsampling module inspired by the Focus network, which is applied to the backbone network. This module compensates for information loss that occurs during conventional convolution and downsampling operations, facilitating a lightweight architecture. We call this downsampling module LAWDS. Additionally, we introduce the concepts of the P2 layer and shared convolution in the detection head of YOLOv8, leading to the development of a lightweight small-target detection head, referred to as P2-LSCSBD. Furthermore, we combine the ideas of WiseIoU, InnerIoU, and MPDIoU to create a novel loss function, which we designate as WIMIoU. This new loss function enhances model accuracy significantly. We evaluate our method on a homemade cigarette detection dataset, comparing it with the baseline YOLOv8 target detection model and conducting ablation experiments. Notably, LSOD-YOLOv8 demonstrates a 2.8-point improvement in mAP50 metrics over the original YOLOv8 while achieving a 20% reduction in the number of parameters. Our proposed method exhibits excellent performance in terms of both accuracy and lightweight design, highlighting its effectiveness for smoking detection. The contributions of this paper are as follows:  
(1) The proposed model incorporates a lightweight adaptive weight downsampling module that addresses the issue of information loss commonly seen in conventional convolution and downsampling operations while simultaneously achieving a more lightweight architecture. (2) The proposed model introduces a lightweight small-target detection head that allows the model to fuse multi-scale information while significantly reducing the number of parameters through shared convolutional information and independent batch normalization operations, thereby enhancing overall accuracy.