UAVSNet: An Encoder-Decoder Architecture based UAV Image Segmentation Network

Due to an increased application of Unmanned Aerial Vehicle (UAV) devices like drones, segmentation of aerial images for urban scene understanding has brought a new research opportunity. Aerial images own so much variability in scale, object appearance, and complex background. The task of semantic segmentation when capturing the underlying features in a global and local context for the UAV images becomes challenging. In this work, we proposed a UAV Segmentation Network (UAVSNet) for precise semantic segmentation of urban aerial scenes. It is a transformer-based encoder-decoder framework that uses multi-scale feature representations. The UAVSNet exploits the advantage of a self-attention-based transformer framework and convolution mechanisms in capturing the global and local context details. This helps the network precisely capture the inherent feature of the aerial images and generate overall semantically rich feature representation. The proposed Overlap Token Embedding (OTE) module generates multi-scale features. A decoder network is proposed, which further processes these features using a multi-scale feature fusion policy to enhance the feature representation ability of the network. We show the effectiveness of the proposed network on UAVid and Urban drone datasets by achieving mIoU of 64.35% and 74.64%, respectively.