In the UAV visual navigation and positioning system, the topological relationship between road intersections can provide an important basis for remote sensing image matching and localization. Therefore, this study proposes a road intersection matching method based on triangular structure, which constructs a semantic expression library of road intersection targets as a benchmark database for road intersection matching; and then formulates a matching strategy based on the triangular similarity principle with the salient features between road intersections to complete the accurate and efficient localization of road intersections. The simulation experiment results show that this algorithm is stable and robust, and is not affected by brightness, noise and view angle. Under the 6Km*6Km large scene matching condition, it takes 7.8S from intersection detection to intersection geolocation localization, correctly matches 8 road intersections, and meets the UAV localization requirements.
The detection of small objects by oriented bounding box in aerial images is a recent hot topic. However, since the aerial images are not collected at the same height, the Ground Sample Distance (GSD) is different for each image, so that small objects are easily overlooked. Existing algorithms are designed for multi-scale object detection, and feature fusion is time-consuming, resulting in a large amount of model parameters that is not easy to deploy on embedded devices. We propose three methods to address the above problems. First, we scale the collected aerial images to the same scale according to the GSD value. Second, we change the structure of Feature Pyramid Network (FPN) and only keep the necessary low-level feature maps. Finally, we rescale the anchor for the specific scene. We validate our proposed method on the DOTA dataset. The results show that the modified model using our method can identify more small-scale objects, and the maximum number of model parameters can be reduced by 2.7%, the inference speed can be increased by 13.24%, and the model size was reduced by up to 28% when the detection accuracy is the same as the original algorithm.
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