Unmanned surface vehicle (USV)-based floating-waste detection presents significant challenges. Due to the water surface’s high reflectivity, there are often light spots and reflections in images captured by USVs. Furthermore, floating waste often consists of numerous small objects that prove difficult to detect, posing a robustness challenge for object-detection networks. To address these issues, we introduce a new dataset collected by USV, FloatingWaste-I, which accounts for the effects of light in various weather conditions, including sunny, cloudy, rainy and nighttime scenarios. This dataset comprises two types of waste: bottles and cartons. We also propose the innovative floating-waste-detection network, YOLO-Float, which incorporates a low-level representation-enhancement module and an attentional-fusion module. The former boosts the network’s low-level representation capability while the latter fuses the highest- and lowest-resolution feature map to improve the model robustness. We evaluated our method by using both the public dataset FloW-img and our FloatingWaste-I dataset. The results confirm YOLO-Float’s effectiveness, with an AP of 44.2% on the FloW-img dataset, surpassing the existing YOLOR, YOLOX and YOLOv7 by 3.2%, 2.7% and 3.4%, respectively.

Efficient cleaning of floating waste using an intelligent unmanned surface vehicle is an important development trend in inland water governance. Path planning is the core of the decision-making module for unmanned surface vehicle waste cleaning and is key to achieving autonomous operation of the unmanned surface vehicle. However, due to the complexity and dynamic changes of the water surface environment, unmanned surface vehicle path planning methods for floating waste face challenges such as small size, uncertainty, and uneven distribution of floating waste. In response to the above issues, this article studies the problem of insufficient integration and low efficiency between existing path planning algorithms and target-perception modules, and designs an efficient overall path planning method for floating-waste cleaning by an unmanned surface vehicle. This method transforms the path planning problem of floating-waste cleaning unmanned surface vehicle into a Traveling Salesman Problem by setting global patrol points and tracking local targets, and proposes an improved ant colony algorithm, IACO, to solve the Traveling Salesman Problem. This article is based on the TSPLIB dataset and practical applications for experiments. The experimental results show that the proposed method has average optimal path lengths of 75.930 m, 446.555 m, and 703.759 m on the Ulysses22, eil51, and st70 datasets, respectively, which are reduced by 0.355 m, 4.108 m, and 13.575 m compared to the benchmark.