# SS-YOLOv8: A Lightweight Algorithm for Surface Litter Detection
## 5 Conclusions  
In this paper, we make improvements for YOLOv8 when deployed at the edge end where the model is too large, the processing speed is too slow, and the real-time effect is not good. To this end, this paper proposes a fast and lightweight SS-YOLOv8 algorithm. First, the WS-C2f module with Sobel_conv was used to replace the C2f module, with the objective of enhancement of backbone feature extraction capabilities. Secondly, in order to ensure the model is lightweight, a detection head SCDH utilizing shared convolutional composition is proposed. Thirdly, the original CIoU loss function is replaced by PIoU2, which improves the localization accuracy of small garbage on the surface of the water while accelerating the convergence speed of the network. Fourth, a slimming pruning method is employed to significantly reduce the number of redundant parameters, thereby reducing the model size. The experiments demonstrate that SS-YOLOv8 exhibits notable enhancements in model size, computational efficiency, and inference speed. The model's mAP50 and mAP50-95 are 79.9% and 51%, respectively, while its memory occupation is reduced by 88.3%. Additionally, the detection speed has been accelerated by 14 frames/s, which considerably simplifies its operation on edge devices while maintaining a minimal degree of accuracy. Subsequent work will concentrate on the implementation of the model on edge devices, with the objective of enhancing the model's detection capabilities in complex airspace and special scenarios.  
Surface litter has a more complex background and is susceptible to weather. The semi-floating trash and the refraction of the water surface can affect the computer's vision. This is the limitation of this model. In the future, high-resolution drones, satellite imagery, and underwater sensors will be capable of monitoring and identifying the type and location of surface debris in real time. The model presented in this paper, when combined with automated garbage sorting and tracking, can facilitate improvements in the accuracy and efficiency of cleanup work. The results of this research have the potential to enhance the quality of life for citizens by improving the living environment.  
Conceptualization, Z.Q. ([PERSON]) and Z.F.; methodology, Z.F.; formal analysis, Z.Q. ([PERSON]) and Z.F.; investigation, Z.Q. ([PERSON]), Z.Q. ([PERSON]), and W.L.; resources, [PERSON] ([PERSON]) and Z.F.; data curation, [PERSON] ([PERSON]); writing--original draft preparation, [PERSON]; writing--review and editing, [PERSON] ([PERSON]) and [PERSON]; visualization, [PERSON]; supervision, [PERSON] ([PERSON]) and [PERSON]; project administration, M.C.; funding acquisition, M.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Heilongjiang Postdoctoral Fund to pursue scientific research (grant number LBH-Z23025), Heilongjiang Province Colleges and Universities Basic Scientific Research Business Expenses Project (grant number 2023-KYYWF-1052), Harbin University of Commerce Industrialization Project (grant number 22 CZ04), and Collaborative Innovation Achievement Program of Double First-class Disciplines in Heilongjiang Province (grant number LJGXCG2022-085).  
Not applicable.  
The surface litter dataset for this paper can be accessed with the URL :[[https://pan.baidu.com/s/1](https://pan.baidu.com/s/1) rkhxL9 Ls_OrdiWwotMffYkw?pwd=u0b0]([https://pan.baidu.com/s/1](https://pan.baidu.com/s/1) rkhxL9 Ls_OrdiWwotMffYkw?pwd=u0b0)* (accessed on 8 October 2024). Additional original contributions presented in this study are included in this article, and further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.