# JANNet: A U-Shape Network with Jump Attention for Semantic Segmentation of Road Scenes
###### Abstract
These network architectures can optimize the semantic segmentation effect very well, but a large amount of computation and a complex network structure also become the disadvantages of such methods. As the network layer deepens, the feature map resolution decreases and contains less spatial detail information, and the reconstruction of the resolution has to be carried out before obtaining the segmentation results. It is difficult for the ordinary FCN network to restore the spatial detail information of the image better, which becomes another difficulty in the semantic segmentation task. Some U-shape structures (i.e., encoder-decoder structures) can better restore the spatial information of images, such as the UNet structure proposed by [PERSON] et al. [14] and the SegNet structure extracted by [PERSON] et al. [15]. Such structures use low-level information to assist higher-level features in recovering image details through an encoder-decoder structure. However, this type of network structure also has certain drawbacks. SegNet only passes the pooling index of the lower layer to the higher layer for upsampling while UNet directly superimposes the lower layer features onto the higher layer features without more processing, which may lead to inconsistent segmentation results of the network for the same target. Networks that also use encoder-decoder structures, such as GCN [16] and DDSC [17], have chosen to use more complex decoder structures to enhance the segmentation, but this also increases the complexity of the network. Combined with the above methods, although the traditional road segmentation methods are simple to calculate and have fair results, the color and texture information cannot further help us segment the image in the face of the complex road scenes in the actual driving process. In contrast, methods based on image filtering, graph theory, or clustering cannot extract further image features, and the segmentation accuracy does not meet the demand well. In addition, because of the high segmentation requirements for off-road targets in today's autonomous driving scenarios, as well as the need to obtain the class to which each segment belongs, traditional methods are no longer adequate for today'sautonomous driving scenarios. For FCN and Deeplab, although better segmentation results can be obtained, the network structure is more complex, and the deep network ignores the shallow feature information, so the final segmentation results are blurred at the edge contour. Although SegNet and UNet fuse the low-level features with the high-level features, they do not process the low-level features, which may bring a lot of useless information to the high-level features and affect the final segmentation results. In order to solve the problem of combining low-level features and high-level features, this paper draws on attention mechanisms such as SE-Net [18] and CBAM [19], which are improved based on UNet and GAU structures [20], and use high-level features to generate channel attention weights to assist low-level features for feature extraction. We also add the ConvLSTM module [21] for extracting temporal contextual information between images to obtain better segmentation results.