Research on semantic segmentation method of urban streetscape image based on deep learning

In computer vision technology, semantic segmentation technology occupies a very important area, which is widely used in driverless and other fields. Semantic segmentation of urban streetscape image is a difficult task, improving segmentation accuracy has been one of the ultimate goal for a long time. There are some problems in segmentation accuracy, including insufficient access to context information and the dim segmentation results at the edge of different objects. Here, based on the full convolution neural network (FCN) in deep learning, we select duel attention network (DANet)1 as our baseline, which introduces attention mechanism to detect context information and its mIoU on Cityscapes reaches 0.646 and pixAcc reaches 0.941. Besides, we try to get richer multiscale context information by replacing the position attention module (PAM) with compact position attention module (CPAM). In addition, we use a loss function based on distance to edge and the number of new pixels to adjust the imbalance between positive and negative samples. Finally, compared to the baseline, the former figure rises 1.5 percent and the latter rises 1.8 percent. The accuracy of semantic edge segmentation is improved.