## 2 An ESNet of Real-Time Semantic Segmentation Based on DCNN
### An ESNet for Semantic Segmentation  
To improve the accuracy of semantic segmentation, an ESNet for real-time semantic segmentation for autonomous unmanned system in traffic scenes is proposed. The results of the specific network are shown in Figure 6.  
The difference between the ESNet network in this paper and the existing methods is mainly reflected in the network design [11; 12; 13]. As discussed in the introduction of this article, the basic framework of SegNet, a classical semantic segmentation network, adopts VGG16 deep learning network as an end-to-end network, which finally classifies images at the pixel level. ENet adopts an asymmetric encoder-decoder structure, effectively reducing the number of parameters. ESPNet uses an efficient spatial pyramid convolution module to improve the operation speed, but the accuracy and semantic segmentation effect need to be optimized. CGNet mainly relies on the context-guided (CG) block structure to improve the detection accuracy, which belongs to the network proposed after optimizing the local structure. ERFNet is similar to ENet, which is improved under the framework of the ResNet network. The operation of low-rank approximation significantly improves the calculation accuracy and leads to many parameter settings required by the network. ICNet adopts the structure of multi-scale prediction fusion and improves the classification accuracy by integrating the low-level semantic information and high-level detail information. Although the above networks are optimized in terms of detection accuracy and efficiency, most semantic  
Figure 6: ESNet structure. segmentation networks are challenging to achieve. Most optimization methods used by semantic segmentation networks mainly include designing high-performance processing structures, using the encoder-decoder structure, and multi-scale prediction fusion. It is not easy to achieve efficient and accurate semantic segmentation by focusing on only one of the optimization methods. Based on this, we propose an ESNet semantic segmentation network by integrating various optimization methods. The ESNet network adopts the above optimization methods, and the independently designed PFCU structure further improves the network's robustness and detection accuracy. Through some optimization design, the network has apparent advantages in the semantic segmentation of the urban environment. The structural design of ESNet network is introduced in detail below. ESNet is an efficient symmetrical encoder-decoder structure consisting of four units: up-sampling, Parallel Factorized Convolution Unit (PFCU), Factorized Convolution Unit (FCU), and down-sampled. Among them, the residual module of the FCU adopts 1D factorized convolution with different sizes of convolution kernel, which enables it to capture object instances of different sizes. In order to effectively improve the feature expression ability of the network, the residual module of the PFCU adopts the strategy of \"transform-split-transform-fusion\". It uses convolution with different atrous coefficients to expand the receptive field of the convolutional layer. The ESNet structure is constructed by symmetrically stacking the residual modules of PFCU and FCU. The encoder generates the deep semantic feature image of the down-sampled unit. In contrast, the decoder up-samples the deep feature image to match the resolution of the input image so that the spatial information of the image is recovered and mapped to the segmentation category. The semantic segmentation prediction results are finally generated, and the obtained prediction results are consistent with the resolution of the input image [35,36]. The structure level of ESNet is shown in Figure 7.  
(1) Hybrid residual block of atrous factorized convolutional encode  
The PFCU is the core component of the designed ESNet structure, and its specific structure is shown in Figure 8. The residual module of the PFCU adopts the strategy of \"transform-split-transform-fusion\".