## 2 Related Work  
The early machine learning methods divided the obstacle detection task into two steps: feature extraction and feature classification. The artificially designed features were input into some shallow classifiers for detection and recognition, and the detection accuracy and classification ability are limited. In recent years, deep learning techniques using deep neural networks as tools have greatly advanced the development of artificial intelligence. Among them, deep convolutional neural networks (CNNs) [6; 7; 8] have a powerful feature extraction capability, which can learn the features of the object better and achieve higher accuracy detection. Therefore, CNNs are widely used in obstacle detection tasks [9; 10; 11]. For example, [PERSON] et al. [9] first targeted obstacle regions by the maximum difference and morphological Region Of Interest (ROI) extraction method and then input the resulting ROI into CNN for obstacle recognition, thus improving the accuracy of obstacle detection and recognition. [PERSON] et al. [10] added the global information of a single image to the classifier while using CNN combined with Deep Belief Network (DBN) to build an obstacle detection model, which experimentally proved to have good detection capability. [PERSON] et al. [11] proposed an obstacle detection and road segmentation method called StixelNet, which simplified the detection task to a stixel regression problem and introduced a loss function based on a semi-discrete representation of the obstacle position probability to train the network, and achieved good results on the KITTI dataset [12]. However, these traditional CNN-based obstacle detection methods are less flexible and cannot provide dense and accurate obstacle locations. An effective way to use CNNs for obstacle detection is semantic segmentation. Semantic segmentation is the classification of each pixel in an image to achieve region segmentation, which is suitable for application in pixel-level scene representation and obstacle detection. In 2015, [PERSON] et al. proposed the Fully Convolutional Network (FCN) [13], replacing fully connected layers in the traditional CNN with convolutional layers. FCN is widely used in semantic segmentation because the convolutional layer can retain a higher image resolution, which is beneficial for pixel-level task such as semantic segmentation. The evaluation results of the PASCAL VOC dataset [14] showed that FCN-based semantic segmentation methods [15; 16] take the lead. [PERSON] et al. proposed SegNet [17] based on FCN, whose encoder part used the first 13 layers of the convolutional network of VGG-16 [18], and each encoder layer corresponded to a decoder layer. The final output of the decoder was fed to a softmax classifier to generate a class probability for each pixel independently. To address the problem of a single scale of FCN feature extraction, the Unet [19] proposed by [PERSON] et al. adopted the feature channel concatenation method, thus retaining more image information. In addition, its unique U-shaped network structure enabled it to have better access to the semantic information of small objects. The DeeplabV3+ model [20] proposed by [PERSON] et al. in 2018 incorporated an Encoder-Decoder structure and combined it with Atrous Spatial Pyramid Pooling (ASPP), which enlarged the perceptual field of the model by introducing atrous convolution and further improved the segmentation capability of the model for objects of different sizes. In recent years, some researchers have applied semantic segmentation models to the field of obstacle detection and achieved good results. For example, [PERSON] et al. [21] proposed a deep residual network EAR-Net for road scene segmentation on the basis of ASPP, which improved the accuracy of segmentation by utilizing depthwise separable convolution and interpolation for feature extraction and recovery. [PERSON] et al. [22] combined depth estimation with a semantic segmentation model and proposed a hybrid CNN network.