One of the most important tasks in the advanced transportation systems is road extraction. Unlike single geospatial objects extraction, extracting road region from high-resolution remote sensing imagery is challenging due to complicated background and rural road networks that have heterogeneous forms with low interclass and high intraclass differences. In the past few years, convolutional neural networks (CNNs) have achieved milestones in road extraction. In particular, the networks based on U -shaped architecture and skip-connections have been widely used in a variety of road extraction tasks. However, although CNN has achieved excellent performance, global and long-range semantic information interaction cannot be learned well due to the locality of convolution operation. In this paper, we propose Swin Transformer Unet which combined U-shaped architecture with hierarchical Swin Transformer with shifted windows. Moreover, we improve the loss function to make it more suitable for our road extraction task. The techniques of data augmentation and data preprocessing are used in order to get better performance. The Massachusetts roads dataset is chosen as the dataset to carry out the experiment of road extraction, and the result shows that this model outperforms other U-shaped networks of road extraction from remote sensing images.

The unmanned aerial vehicles (UAV) swarm formation has drawn the attention of researchers in various military and civilian domains. Formation transformation is also a significant issue for the swarm of UAV. This article studies the transformation of UAV formation problem, and a modified formation transformation method based on genetic algorithm for UAV swarm in 3D environment is developed to improve the efficiency of UAV formation. In our method, firstly, variable mutation rate is used in the genetic algorithm to increase or reduce the mutation rate in the iteration of the algorithm. At the same time, the population is grouped before the crossover operation according to the fitness value. These strategies can not only maintain the speed of the algorithm, but can also avoid the premature convergence to the local optimum and find a better solution. Then, the shortest total flight length of UAV swarm and the shortest formation completion time are formalized as two different problems respectively. Comparison experiment indicated that our proposed methodology can provide a better solution in comparison to conventional methods and particle swarm optimization algorithm. Finally, the comprehensive experiment in UAV simulation platform, named XTDrone, is conducted and the feasibility of the proposed method is validated.

The main objective of scene text recognition is to recognize text in complex images and convert it into editable text. However, scene text recognition research has long been focused on English, and there is a lack of research on other small languages, such as Uyghur language. This paper conducts a series of studies on Uyghur text recognition in natural scenes, with the following main contributions:(1) To address the lack of Uyghur scene text recognition datasets, we established the Synthetic Uyghur Scene Text dataset SUST and the Real Uyghur Scene Text dataset RUST, and we augmented RUST with STR-Aug. (2) The contemporary existing STR models are selected to conduct experiments on the dataset proposed in this paper, through which we search for the model structure suitable for Uyghur scene text recognition. (3) According to the characteristics of Uyghur text, this paper designs a lightweight Uyghur text recognition model named LUSTR, which takes into account both lightweight and accuracy, and achieves good performance in the Uyghur scene text recognition dataset proposed in this paper. The SUST and RUST are now publicly available at https://github.com/kongfnajie/SUST-and-RUST-datasets-for-Uyghur-STR.

Aiming at the problems of Uyghur oblique deformation, character adhesion and character similarity in scene images, this paper proposes a scene Uyghur recognition model with enhanced visual prediction. First, the content-aware correction network TPS++ is used to perform feature-level correction for skewed text. Then, ABINet is used as the basic recognition network, and the U-Net structure in the vision model is improved to aggregate horizontal features, suppress multiple activation phenomena, better describe the spatial characteristics of character positions, and alleviate the problem of character adhesion. Finally, a visual masking semantic awareness (VMSA) module is added to guide the vision model to consider the language information in the visual space by masking the corresponding visual features on the attention map to obtain more accurate visual prediction. This module can not only alleviate the correction load of the language model, but also distinguish similar characters using the language information. The effectiveness of the improved method is verified by ablation experiments, and the model is compared with common scene text recognition methods and scene Uyghur recognition methods on the self-built scene Uyghur dataset.