# SS-YOLOv8: A Lightweight Algorithm for Surface Litter Detection
## 1 Introduction  
The meteorological conditions, which have become increasingly erratic, have resulted in the accumulation of waste material in river systems. Surface garbage is a significant contributor to river pollution, disrupting the ecological balance of rivers and causing the death of vast numbers of river organisms. This, in turn, affects the ecological balance of rivers and the living environment of people in riverine areas. The recycling and cleaning of river garbage is an essential step in maintaining the ecological balance of the planet. Nevertheless, this process is inherently time consuming and labor intensive. In recent years, a substantial body of research has been conducted on target detection in the domain of deep learning, as evidenced in [1; 2; 3; 4; 5]. These methods have the capacity to extract features from images and have demonstrated notable efficacy in target detection, image classification, and target localization. However, these models suffer from several limitations, including the use of large models, parameter redundancy, limited robustness, and low efficiency. Furthermore, they are not suitable for deployment on edge devices. In recent years, studies [6; 7; 8] have shown that deploying neural network models on edge devices is economical and applicable. The combination of high-resolution cameras and the compact size of edge devices allows for the effective detection of surface litter. Furthermore, the flexibility of the edge device, which has a minimal environmental impact, makes it a sustainable tool for the ecological maintenance of rivers. The ability to analyze real-time data enhances the timeliness of decision-making and effectively reduces the waste of labor. Thus, the deployment of the network model at the edge facilitates a shift towards moreefficient, safe, and environmentally friendly practices in the field of river ecological surface waste detection. In recent years, deep learning has become more and more important in the field of object detection, which is proved by the literature [9]. Application of deep learning neural networks for target calling algorithms has demonstrated considerable promise in the automatic detection of rubbish, offering real-time, robust, and widely available solutions. Nevertheless, the extant algorithmic models remain susceptible to improvement with respect to the detection of water surface litter. The traditional Faster R-CNN [10] is afflicted by the vices of parameter redundancy, a substantial model, and low efficiency. Classical single-stage algorithms, such as YOLOv8 [11], have demonstrated notable efficiency gains. However, they remain suboptimal in terms of accuracy and are characterized by large models that present challenges for deployment on edge devices. It is commonly acknowledged that traditional convolutional neural networks (CNNs) often necessitate substantial computational resources and parameters to achieve satisfactory performance. This presents a significant challenge in terms of deploying such models on edge devices, which are often constrained by limited memory and computational capabilities. Accordingly, our objective is to develop a lightweight and highly performant model for the detection of water surface litter. [PERSON] [12] made a number of comparisons of deployments at the edge of some of the columns. The optimized model deployed in all the Jetson series achieved an average percent power reduced (%R) of 53.18% with a percent difference of 35.9% from the results of the local PC. Despite the success of lightweight neural networks in various industries, research in river water surface litter detection is quite limited. This study aims to address this gap by developing an efficient and lightweight surface waste detection model. The contributions of this study are as follows:  
1. It is proposed that the faster implementation of the CSP Bottleneck with convolutions and the Sobel convolutions (Sobel_Conv) C2f-WS module be used for the detection of real water surface litter, based on the C2f module in YOLOv8.