## 1 Introduction  
Salvage of floating objects is an important part of water source conservation. At present, interception and manual salvage are the main methods for the salvage task [1]. These methods are labor consuming and dangerous. Although manipulating robots overcomes the shortcomings noted above [2; 3], it requires skilled operators and cannot operate around the clock. With the development of image processing and computer vision, the use of autonomous salvage technology in surface cleaning robots has attracted great interest from researchers, and one of its most important elements is the fast and robust detection of floating objects. However, small sample acquisition is a tough problem due to the diversity and rarity of floating objects in different water areas. Additionally, interference factors such as ripple, reflection, and uneven illumination also make it difficult to find a general model for various complex water scenarios. Few researchers have focused on floating object detection with respect to complex water surfaces. In general, the existing methods that can be applied to the detection task fall into four categories: traditional image segmentation, saliency detection, hand-crafted feature detection, and object detection based on a Convolutional Neural Network (CNN). According to different processing domains, these methods can also be classified into spatial-based methods and frequency-based methods. In spatial domain, traditional image segmentation has an excellent real-time performance [4]. [PERSON] et al. [5] extracted contours of floating objects according to image grayscale, then framed the contours that met a special size criterion. [PERSON] et al. [6] used the Super-pixel Merging method to segment eligible foreground regions. [PERSON] et al. [7] used Mean-shift clustering and an improved Otsu [8] method to detect floating objects. Although these methods work well in calm water, they are not robust enough when ripples or reflections exist. To this end, [PERSON] et al. [9] proposed an improved Gaussian Mixture Model Based (GMM-based) automatic segmentation method (IGASM) to detect water surface floats, but it is not applicable in static images or in images with severe interferences. Spatial-based object detection methods can be applied to floating object detection and have a certain anti-interference ability [10; 11]. Traditional methods of combining hand-crafted features with classifiers are simple and fast [12]. Hand-crafted texture features with grayscale invariance, e.g., Histogram of Oriented Gradient (HOG) [13], Local Binary Pattern (LBP) [14], and Gray Level Co-occurrence Matrix (GLCM) [15], are suitable for describing floating objects under uneven illumination. But these features do not utilize global information, making feature descriptors inaccurate when other sever interferences exist. Therefore, it is necessary to extract very suitable features with respect to the special detection task, i.e., floating object detection in complex water scenes. Some algorithms based on CNN do not need to manually extract features and perform well in speed and accuracy, e.g., a Region-based Fully Convolutional Network (R-FCN) [16], Single Shot Multi-Box Detector (SSD) [17], Single Shot Refinement Detector (RefineDet) [18], and the You Only Look Once (YOLOv3) method [19]. However, in the case of a small sample, CNN-based methods are prone to over-fitting due to their multi-layer convolutions. To this end, saliency detection methods based on visual attention mechanism can solve the problem of small sample. But spatial-based saliency detection methods, e.g., Global Contrast-based Saliency Detection (RC) [20], Contrast-based Filtering for Saliency Detection (SF) [21] and Boolean Map for Saliency Detection (BMS) [22] are generally sensitive to parameter selection, which limits their generalization in multiple scenarios.