###### Abstract  
Segmentation of street scenes is a key technology in the field of autonomous vehicles. However, conventional segmentation methods achieve low accuracy because of the complexity of street landscapes. Therefore, we propose an efficient atrous residual network (EAR-Net) to improve accuracy while maintaining computation costs. First, we performed feature extraction and restoration, utilizing depthwise separable convolution (DSConv) and interpolation. Compared with conventional methods, DSConv and interpolation significantly reduce computation costs while minimizing performance degradation. Second, we utilized residual learning and atrous spatial pyramid pooling (ASPP) to achieve high accuracy. Residual learning increases the ability to extract context information by preventing the problem of feature and gradient losses. In addition, ASPP extracts additional context information while maintaining the resolution of the feature map. Finally, to alleviate the class imbalance between the image background and objects and to improve learning efficiency, we utilized focal loss. We evaluated EAR-Net on the Cityscapes dataset, which is commonly used for street scene segmentation studies. Experimental results showed that the EAR-Net had better segmentation results and similar computation costs as the conventional methods. We also conducted an ablation study to analyze the contributions of the ASPP and DSConv in the EAR-Net. Keywords:atrous spatial pyramid pooling; deep learning; encoder-decoder; residual learning; semantic segmentation +
Footnote †: journal:  
outputs a segmentation map. The context information is essential for classifying object categories, and the segmentation map indicates the category to which each pixel in the image corresponds. FCN was first proposed as a deep learning-based semantic segmentation method and presented a strategy for using a network for classification in segmentation. However, in FCN, the outline or detailed information is lost in the process of generating the segmentation map; thus, the objects in the image are inaccurately divided. Therefore, various methods, such as U-Net, SegNet, and DeepLab, were proposed to solve this problem. The U-Net is a concatenation operation [19], which minimizes information loss by utilizing the intermediate feature information of the encoder in the decoder. As a result, segmentation accuracy is improved, compared to the FCN. However, U-Net has a problem in that object segmentation is incorrect or fails due to a lack of context information extracted from the encoder. This problem hinders the recognition of the position and motion of the objects in the application field of autonomous vehicles and increases the error of the position and size of lesions when applied to medical image analysis. To improve the segmentation accuracy, four versions (V1, V2, V3, V3+) of DeepLab were developed, and DeepLab continues to be studied: DeepLabv1 proposed an atrous convolution that can increase the kernel size while maintaining the computation costs and applied it to a CNN; DeepLabv2 proposed atrous spatial pyramid pooling (ASPP) to utilize multi-scale features by applying atrous convolution; DeepLabv3 improved the segmentation accuracy by analyzing and improving the previously proposed methods; and DeepLabv3+ improved the decoder and used a modified Xception [20] network as its backbone network. However, the above methods focus on accuracy and are difficult to use in mobile or embedded devices because of their high computation costs. To solve this problem, ENet [21], ICNet [22], and others are used to minimize the computation costs. In the ENet, a tiny encoder-decoder was designed to achieve very small computation costs. However, as there is a trade-off between accuracy and computation costs, the accuracy was significantly reduced. The accuracy reduction due to the trade-off was minimized in the ICNet by using a multi-scale input strategy and cascaded network. A method proposed by [PERSON] et al.