Pixel-level semantic segmentation based on gradient features

Due to the mechanism of pooling and convolutional layers, many important features and the correlation between the features are lost in the forward propagation process in the pixel-level semantic segmentation tasks. Therefore, here we analyze the edge features of the image by means of second-order difference, propose gradient features and design the corresponding gradient convolution layer. Based on the gradient convolution layer, we use the residual structure to achieve the fusion of high-resolution gradient features and low-resolution gradient features. Finally, we designed the GraDNet. In the tests on the Cityscapes and ADE20K datasets, GraDNet achieves the best results in both accuracy and speed compared to some SOTA algorithms.