# Salient Semantic Segmentation Based on RGB-D Camera for Robot Semantic Mapping
## 1 Introduction
Such as, in related works, the salient object detection method [23] runs at 300 FPS, at the maximum dimension of input images is 300 pixels. This paper's model [24] runs at 57 FPS while producing better detection results than previous state-of-the-art methods. The YOLO V3 [25] for object recognition runs at 45.5 FPS at an input size of 320 \(\times\) 320. However, FPS in semantic segmentation is generally small [26] compared to the above, such as the representative work among them FCN-8s [5], SegNet [8], PSPNet [6], DeepLab-v2 [7] respectively run at 15, 17, 5, 6 FPS on NVIDIA Titan. Semantic mapping based on semantic segmentation has not been applied in the industrial field, but object recognition used to detect defective items on the production line and salient object detection used as the preprocessing stage of high-order tasks have been applied in the industrial field. The above-related works prove that the method proposed in this paper is feasible in theory. The contour shape feature is the most crucial object feature, and it is relatively stable [27; 28]. It is not be affected by the objects in different categories with similar color and texture characteristics and indistinguishable. The grayscale image has fewer data dimensions than the RGB image and has fewer feature details that can easily be learned by the neural network, which is used in this paper and can reduce the calculation amount. For the above reasons, we used grayscale images with object contours as the training data to train the neural network model based on salient object contours to recognize objects for the semantic annotation of salient objects' contour segmentation in the process of mobile robot RGB-D mapping. Furthermore, we chose YOLO to improve the running speed further to meet the real-time requirements of semantic segmentation when building maps. YOLO uses a single-pipe strategy, and its training and prediction are both end-to-end, so the YOLO algorithm is relatively simple and fast compared with a variety of neural network models [29; 30; 31]. YOLO convolves the picture with a larger field of view used for object recognition, and it is not easy to misjudge the background [32; 33]. YOLO has a strong generalization ability and hashigh robustness when doing transfer learning. In the YOLO series, YOLO V3 is the Most Classic balanced object recognition network in terms of speed and accuracy by far, and it is the peak of the YOLO series. The new YOLO series are all perfected and supplemented based on YOLO V3 by the fusion of various advanced methods for improvement [34; 35]. YOLO V3 has irreplaceable advantages for real-time object recognition applications in the industry. It has the most potential to meet the real-time demand for semantic annotation in mobile robot mapping. RGB-D camera can acquire the depth information of the RGB image. This makes up for the shortcomings of (1) the uncertainty of the monocular camera scale and (2) the binocular camera, which has a large calculation amount when calculating the depth. The RGB-D camera is becoming the mainstream sensor in the field of visual SLAM research [36; 37; 38; 39]. First, this paper proposes detecting the salient objects in the RGB-D image and recognizing the salient objects by the trained neural network model YOLO V3 based on the contour shape feature. Furthermore, we extracted the salient object contours in the grayscale image and projected them into the corresponding RGB image, which realized the salient objects' semantic segmentation in the RGB-D image's RGB part used for mobile robot mapping. The organization of this paper is as follows: The relevant algorithms are analyzed and connected with the research points of our work in the second part. The main theory of the proposed method is described in the third part. The proposed method's experimental details and conclusion analysis are in the fourth part.