In order to address the problems of various interference factors and small sample acquisition in surface floating object detection, an object detection algorithm combining spatial and frequency domains is proposed. Firstly, a rough texture detection is performed in a spatial domain. A Fused Histogram of Oriented Gradient (FHOG) is combined with a Gray Level Co-occurrence Matrix (GLCM) to describe global and local information of floating objects, and sliding windows are classified by Support Vector Machines (SVM) with new texture features. Then, a novel frequency-based saliency detection method used in complex scenes is proposed. It adopts global and local low-rank decompositions to remove redundant regions caused by multiple interferences and retain floating objects. The final detection result is obtained by a strategy of combining bounding boxes from different processing domains. Experimental results show that the overall performance of the proposed method is superior to other popular methods, including traditional image segmentation, saliency detection, hand-crafted texture detection, and Convolutional Neural Network Based (CNN-based) object detection. The proposed method is characterized by small sample training and strong anti-interference ability in complex water scenes like ripple, reflection, and uneven illumination. The average precision of the proposed is 97.2%, with only 0.504 seconds of time consumption.