# Encoder-Decoder Structure Fusing Depth Information for Outdoor Semantic Segmentation
## 1 Introduction  
Image semantic segmentation plays a critical role in computer vision tasks, an interdisciplinary subject in the fields of machine learning, artificial intelligence and computer vision and involves several computer techniques such as image recognition, image understanding and analysis. High-level semantic labels are assigned to each pixel in an image, that is, each pixel is classified. Semantic segmentation is a fundamental technology for scene understanding and plays a vital role in areas such as autopilot [1], autonomous navigation [2], image medical treatment [3], UAV landing and daily life [4]. Traditional semantic segmentation is mainly used to extract the low-level and intermediate features of images, including segmentation algorithms based on the threshold, region, watershed and graph theory [5; 6; 7]. With advancements in computing performance, semantic segmentation using machine learning methods began to be included within the research scope of many initiatives. Machine learning methods mainly use traditional classifiers to classify images, which mainly include random decision forests (RF) [8; 9] and supportvector machines (SVM) [10], but these methods mainly divide data into two categories. The formal application of deep learning in the field of semantic segmentation occurred with the emergence of fully convolutional neural networks (FCN), which can clearly distinguish the categories of objects in an image, such as cats and dogs. FCN can learn pixel-to-pixel mapping, and the size of the input image can be arbitrarily set [11]. This method uses VGG-16 [12] as the basic network to extract features, replaces the fully connected layers with a decoder consisting of deconvolution and convolution for up-sampling, and refines low-resolution feature maps to generate a dense prediction. However, the FCN method has two disadvantages. The first is that the output result is too small, and the spatial features of some pixels are lost in the pooling process. The second is that the context and spatial location information are not fully considered. [PERSON] et al. [13] proposed a new loss, called affinity regression loss (AR loss), to improve the training speed of the semantic segmentation model without relying on contextual information to improve accuracy. Contextual information has been proven to be a powerful clue in semantic segmentation [14; 15]. [PERSON] et al. [14] suggested that objects with similar appearances are one of the challenges of semantic segmentation tasks. Therefore, they proposed a novel context-based tandem network (CTNet) to mine spatial and channel context information to obtain better semantic segmentation results. [PERSON] et al. [15] argued that continuous down-sampling operations lead to a loss of spatial detail information in the image. Therefore, they proposed a multi-context refinement network (MCRNet) to fuse contextual information for pixel-level semantic segmentation. However, these related methods require the design of additional modules to extract contextual information, which increases the complexity of the model. Recent related work solved these problems using RGB-D data. The main advantage is attributed to the depth of information in the scene being less affected by illumination and other conditions, which can improve the performance of semantic segmentation tasks. [PERSON] et al. [16] proposed real-time RGB-D fusion semantic segmentation, called RFNet. The model can effectively utilize complementary block modal information to meet the requirements of automatic driving. [PERSON] et al. [17] proposed an attention complementary network (ACNet) that extracts weighted features from RGB images and depth maps, respectively, and fuses them to solve the problem of unequal amounts of information contained in RGB and depth images. [PERSON] et al. [18] proposed an asymmetric encoder structure for RGB-D indoor scene understanding, which can reduce the difference between low-level and high-level features so as to better fuse features for segmentation. [PERSON] et al.