# JANNet: A U-Shape Network with Jump Attention for Semantic Segmentation of Road Scenes
###### Abstract  
The task of complex scene semantic segmentation is to classify and label the scene image pixel by pixel. For the complex image information in autonomous driving scenes, its characteristics such as many kinds of targets and various scene changes make the segmentation task more difficult, making various kinds of FCN-based networks unable to restore the image information well. In contrast, the encoder-decoder network structure represented by SegNet and UNet uses jump connections and other methods to restore image information. Still, its extraction of shallow details is simple and unfocused. In this paper, we propose a U-shaped convolutional neural network with a jump attention mechanism, which is an improved encoder plus decoder structure to achieve semantic segmentation by four times of convolutional downsampling and four transposed convolutional upsamplings while adding a jump attention module in the upsampling process to realize selective extraction of contextual information from high-dimensional features to guide low-dimensional features, improve the fusion of deep and shallow features, and ensure the consistency of the same type of pixel prediction. The CamVid and Cityscapes datasets are sampled for the experiments, and the model ground mIoU evaluation metrics can reach 66.3% and 69.1%. Compared with other mainstream semantic segmentation algorithms, this method is competitive in terms of segmentation performance and model size. semantic segmentation; convolutional neural network; autopilot 20231314931314931  
used edge detection operators are Sobel, Prewitt, and other operators. After calculating the texture or edge features of the road, threshold segmentation can be used to obtain the road segmentation results. In addition, support vector machines and other methods can be used to achieve better segmentation results. Since parallel straight lines exist at the edges of standard roadways, the intersection points of multiple pairs of parallel straight lines (road edge lines and lane lines) can be extracted, and the center positions of these intersection points can be obtained to approximate the extinction points to segment the roadway from other scenes. With the rapid development of convolutional neural networks (CNNs) [2; 3], their excellent hierarchical feature extraction capabilities and end-to-end training structure have led to significant advances in CNNs for pixel-level semantic segmentation tasks as well [4; 5]. [PERSON] et al. proposed fully convolutional neural networks (FCN) in 2014 [6]. In the FCN model, the fully-connected layer used for classification is changed to a convolutional layer, followed by upsampling of the feature maps using transposed convolution [7]. Finally, a series of classification output maps equal in size to the original image are output, and the maximum value is taken as the prediction class after passing through the Softmax layer. In this way, the FCN model can classify images at the pixel level and complete the semantic segmentation task. The FCN method has achieved superior results in semantic segmentation tasks after its emergence due to its superior results and end-to-end training structure, which, in turn, gradually replaced the traditional image segmentation methods. [PERSON] et al. also used FCN's decoder network for tasks such as optimizing transmission systems [8]. In the following years, to solve the problem of special detection learning for multi-scale targets, [PERSON] et al. designed the Deeplab network architecture [4; 9]. Its multi-scale feature extraction is achieved by using different null convolutions (atrous conv). PSPNet [10] designed a spatial pyramid pooling (SPP) structure to pool different size targets onto the same feature space. MLNet [11] and MANet [12] also use multi-scale feature fusion models to optimize image segmentation tasks. [PERSON] et al. also involved a two-branch network model for image segmentation [13].