# EXOLOv3: An Efficient Real-Time Detection Model for Floating Object on River
## 1 Introduction  
With the rapid development of industrialization and urbanization, the ecological environment has been seriously damaged, especially the water environment. A large number of floating objects such as plastic bottles, plastic bags, and aquatic plants appear on the water surface of many rivers and lakes. The presence of floating objects on the water surface will not only affect the aesthetics of the water body, cause water environment pollution, destroy the ecological balance, and even pose a threat to people's drinking water safety [1]. In addition, a large number of floating objects may affect equipment working in the river. For example, the propeller of a ship may be entangled in floating objects, and floating objects gathered in front of the barrage also have varying degrees of impacts on power generation, shipping, and other aspects [2]. In order to address the pressing water environmental problems, we should develop more intelligent methods to identify and clean up floating objects on the river. At present, the surveillance of the river floating in China is labor-intensive, time-consuming, and may miss something, so a fast and accurate automatic detection method is necessary [3, 4, 5]. With the improvement of science and technology, the performance of computer hardware, especially graphics processing unit, has been greatly improved, thus promoting the rapid development of pattern recognition, computer vision and other deep learning technologies. Therefore, object detection methods based on deep learning have been applied in many fields [6, 7], such as face recognition [8, 9], vehicle detection [10], automatic driving [11, 12], and achieved good results. However, there is little research in the detectionof floating objects on rivers and lakes. The purpose of this paper is to detect the floating objects in the images through deep learning, realize the intelligent monitoring of floating objects on rivers or lakes, so as to reduce the pollution of the water environment caused by the accumulation of floating objects, and lay a foundation for the realization of early warning of water pollution, the improvement of drinking water safety, and good water environment detection system in the future. The early object detection methods, such as the combination of artificial design features and support vector machines, or background difference method, are mainly used for object detection. However, due to the complex environment and diverse forms of floating objects on river, the expression ability of artificial design features on floating object features is insufficient, and the robustness and generalization performance are poor, which cannot achieve the desired detection accuracy [13; 14]. Deep learning methods show more advantages over prior methods in object detection. However, the object detection based on deep learning has high requirements on the quantity and quality of training dataset, while there are few open floating object dataset currently. Therefore, it is a challenging task to create a floating object dataset, and design a network model with high detection accuracy and fast detection speed. In order to solve the above problems, we propose a model named EYOLOv3, an efficient real-time detection model for floating detection based on YOLOv3, and the contributions of this model can be summarized as follows:  
1. In order to make full use of the information of different scale features of floating objects, a multi-scale feature extraction and fusion network is constructed to obtain more scale feature maps. At the same time, the high-level feature maps and low-level detection feature maps are fused to enrich the semantics of feature maps and improve the detection accuracy of our model;
2. In view of the mismatching between the large difference of floating size and the fixed anchor box sizes in YOLOv3, an adaptive anchor box clustering method is proposed to design the anchor box sizes that conforms to the floating objects on the river;
3.