Research on Deep Learning-based Semantic Segmentation Algorithm for UAV Images

Most of the current image semantic segmentation algorithms on UAV vision segment remote sensing images, which cannot represent ground detail information, resulting in obstacles to real-time autonomous environment perception of UAVs in low altitude flight missions. To address this problem, this paper proposes a real-time image semantic segmentation method for low altitude UAVs. The network designs a novel hypernetwork structure that incorporates a context header weight generation module at the last layer of the encoder, the weights of each block in the decoder are generated before the end of encoding in the encoder to reduce the number of parameters and computation of the model to achieve real-time segmentation. In the decoder, a dynamic patch-wise convolutional algorithm is designed using the locally connection layer mechanism to take full account of the contextual semantic information when targeting large segmented objects that are in more than one piece, so that the decoder's weights change with the spatial location of the input feature map, and at the same time, the dynamic weights are used to target the segmentation of different objects, to maximise the network's adaptive nature. In order to verify the effectiveness of the method, this experiment uses the transfer learning technique to carry out pre-training on Cityscape data set, and uses the UAVid data set to validate the method of this paper, and the experimental results show that the mean intersection over union of this method is 66.3% for the images of the categories of buildings, roads, and static cars, and the prediction speed reaches 37.9 FPS, which significantly improves segmentation accuracy under the condition of guaranteeing the real-time performance.