Cross-view geolocalization matches the same target in different images from various views, such as views of unmanned aerial vehicles (UAVs) and satellites, which is a key technology for UAVs to autonomously locate and navigate without a positioning system (e.g., GPS and GNSS). The most challenging aspect in this area is the shifting of targets and nonuniform scales among different views. Published methods focus on extracting coarse features from parts of images, but neglect the relationship between different views, and the influence of scale and shifting. To bridge this gap, an effective network is proposed with well-designed structures, referred to as multiscale block attention (MSBA), based on a local pattern network. MSBA cuts images into several parts with different scales, among which self-attention is applied to make feature extraction more efficient. The features of different views are extracted by a multibranch structure, which was designed to make different branches learn from each other, leading to a more subtle relationship between views. The method was implemented with the newest UAV-based geolocalization dataset. Compared with the existing state-of-the-art (SOTA) method, MSBA accuracy improved by almost 10% when the inference time was equal to that of the SOTA method; when the accuracy of MSBA was the same as that of the SOTA method, inference time was shortened by 30%.

It is a challenging task for unmanned aerial vehicles (UAVs) without a positioning system to locate targets by using images. Matching drone and satellite images is one of the key steps in this task. Due to the large angle and scale gap between drone and satellite views, it is very important to extract finegrained features with strong characterization ability. Most of the published methods are based on the CNN structure, but a lot of information will be lost when using such methods. This is caused by the limitations of the convolution operation (e.g. limited receptive field and downsampling operation). To make up for this shortcoming, a transformer-based network is proposed to extract more contextual information. The network promotes feature alignment through semantic guidance module (SGM). SGM aligns the same semantic parts in the two images by classifying each pixel in the images based on the attention of pixels. In addition, this method can be easily combined with existing methods. The proposed method has been implemented with the newest UAV-based geo-localization dataset. Compared with the existing state-of-the-art (SOTA) method, the proposed method achieves almost 8% improvement in accuracy. INDEX TERMSCross-view image matching, geo-localization, UAV image localization, deep neural network.

Regular inspection of distribution line is an important link to maintain the normal operation of distribution network. Using unmanned aerial vehicle (UAV) instead of manpower can save the cost of inspection. With the universal application of vision sensor in UAV and the rapid development of deep learning, Convolutional Neural Networks (CNN) is applied to the detection of power line in UAV visible images. In view of the lack of application environment inspection methods for distribution line, a vision-based UAV distribution line inspection method using deep learning and a dataset for the deep learning method of distribution line inspection task are proposed in this paper. The method proposed predicts distribution line area through the encoder-decoder structure network firstly. Image processing operation and sampling clustering are used to remove the interference. Finally, the UAV tracking direction of distribution power line is calculated according to the detected distribution line. The method can reach an inspection speed of nearly 77ms per frame, the range of heading deviation error can reach (-1.52°, 1.36°), the tracking rate nearly 100%. Through the test of network and inspection method on dataset, the results show that the method proposed in this paper can be effectively, quickly and accurately applied to UAV distribution line inspection.