We propose a novel effective algorithm for real-time semantic segmentation of images that has the best accuracy in its class. Based on a comparative analysis of preliminary segmentation methods, methods for calculating attributes from image segments, as well as various algorithms of machine learning, the most effective methods in terms of their accuracy and performance are identified. Based on the research results, a modular near real-time algorithm of semantic segmentation is constructed. Training and testing is performed on the ISPRS Vaihingen collection of aerial photos of the visible and IR ranges, to which a pixel map of the terrain heights is attached. An original method for obtaining a normalized nDSM for the original DSM is proposed.
Current neural network-based algorithms for object detection require a huge amount of training data. Creation and annotation of specific datasets for real-life applications require significant human and time resources that are not always available. This issue substantially prevents the successful deployment of AI algorithms in industrial tasks. One possible solutions is a synthesis of train images by rendering 3D models of target objects, which allows effortless automatic annotation. However, direct use of synthetic training datasets does not usually result in an increase of the algorithms’ quality on test data due to differences in data domains. In this paper, we propose the adversarial architecture and training method for a CNN-based detector, which allows the effective use of synthesized images in case of a lack of labeled real-world data. The method was successfully tested on real data and applied for the development of unmanned aerial vehicle (UAV) detection and localization system.
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