Two-level technology of intelligent application of on-board video camera of unmanned aerial vehicle for monitoring of geospatial data
DOI:
https://doi.org/10.18372/2310-5461.47.14873Keywords:
unmanned aerial vehicle, stabilization, disturbances, video data, control system, image selectionAbstract
The main reasons for the deterioration in the quality of images obtained from an unmanned aerial vehicle (UAV) are wind disturbances, as well as a change of the position of the UAV during maneuvering. Traditional methods of trajectory stabilization used in robotics do not work well enough for UAVs. In this regard, there is an urgent task of developing algorithms for reducing the effect of wind disturbances and stabilizing the flight trajectory of UAVs, as well as creating methods for eliminating low quality images already obtained as a result of various types of disturbances.
In this paper, a two-level technology is proposed. This technology determines the critical parameters of the movement of unmanned aerial vehicles, performs spatial compensation of the influence of wind disturbances in both the vertical and horizontal planes for these parameters, and improves the quality of the data stream of the on-board video camera by selecting high-quality images suitable for further processing.
The first level of the proposed technology provides a decrease in the effect of wind disturbances on the UAV flight trajectory. The second level categorizes the flow of images of an onboard video camera for geospatial monitoring as a result of using a high-speed integral stability criterion, which eliminates in real time low quality images from the data flow from the onboard video camera.
The developed system for intelligent control of UAVs and procedures for selecting images by color and sharpness make it possible to successfully solve problems that are largely similar to those that a human expert can face when solving intellectual problems of processing and filtering video information. Therefore, these methods, algorithms and procedures can be implemented in advanced systems of intelligent control in the field of modeling the conscious behavior of a person for the selection of data necessary to perceive the characteristics of the external environment.
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