## 3 Experiments
### Intuitive Results of Texture Detection  
To intuitively compare the effects of our feature with other mainstream features (LBP [14], HOG [13], GLCM [15], FHOG [29]) in the task of floating object detection, a comparative experiment is conducted. The experiment occurred under seven challenging water scenes, including large waves, circular ripples, spray, turbulent water surface, strip ripples, near-view reflection, and far-view reflection. In this comparative experiment, the feature extraction method was the only variable, so it was necessary to keep sample generation and classification methods consistent.  
**Sample generation.** The positive samples are not directly cropped from ground truth labels, because one of the most important features of our classification was is the dramatic change of texture features. Instead, positive samples were generated by cropping special images blocks from the 280 source images. These image blocks had the same centers of the corresponding ground truth labels, while their sizes are all \(48\times 48\) pixels. From each source image, negative samples were generated by cropping sliding image blocks (\(48\times 48\) pixels in size and 20 pixels in stride) that did not intersect with any ground truth labels.  
**Classification.** As mentioned in the Section 2.1, Latent SVMs are adopted as our classifiers for the small sample problem. The uniformed sample size (i.e., \(48\times 48\) pixels) guaranteed the same feature dimensions, which as beneficial for SVM training. In addition, the cell sizes of HOG, FHOG and LBP were all \(16\times 16\) pixels, and the calculation stride of GLCM was 2 pixels. More details of SVM training  
Figure 4: Experimental platform: unmanned surface cleaning robot.  
(built by the Application Programming Interface (API) of opencv4.0.1) are given: the SVM type was C-Support Vector Classification (C-SVC) [27], kernel was Linear [28], and the termination criteria was 1000 iterations or the accuracy reached \(1+1.192092896^{-7}\).  
The detection strategy was to classify multi-scale sliding windows in the testing set. We adopted three scaling ratios: 0.8, 1, and 1.2. The initial window size was \(48\times 48\) pixels and the strides were all 10 pixels. Figure 5 shows the results.  
Figure 5: Results of five texture detection methods. They are (**a**) Histogram of Oriented Gradient (HOG), (**b**) Fused Histogram of Oriented Gradient (FHOG), (**c**) Gray Level Co-occurrence Matrix (GLCM), (**d**) Local Binary Pattern (LBP), (**e**) ours (FHOG-GLCM). From top to bottom, the scenes are large waves, circular ripples, spray, turbulent water surface, strip ripples, near-view reflection, and far-view reflection.  
From Figure 5a,b, it can be seen that FHOG features make the result more accurate than HOG features. Figure 5b-d show that the three methods (FHOG, GLCM, and LBP) have plenty of false detections, but the results of FHOG and GLCM are better than those of LBP, indicating that the two features we selected are effective. Figure 5e shows that our combined features make the detection task in complex scenes more accurate, and greatly reduce false detections. Inevitably, our texture detection results are larger than their ground truths, as there were too few training samples to set small sliding windows. However, floating objects can be correctly detected, and the problem of texture detection will be effectively solved by saliency detection and combination.