The aim of the study is to estimate the MALE-class (Medium Altitude Long Endurance) UAV(Unmanned Air Vehicles) using possibility to solve the problem of regular aerial survey of hugeareas relative to other means used for this, such as: small-sized UAVs, satellite remote sensingand manned aircrafts. Considered is the issue of practical construction of onboard computer visionsystem based on a UAV “Orion” wit a ta eoff weig t of more t an a ton, w ic pro idesaerial photography in the visible and near infrared range and airborne laser scanning of the underlyingsurface with automatic processing of the received data on board in near real-time modedetecting the changes occurred since the previous survey. It has been determined the key componentsof the computer vision system both the hardware and software platform required highperformancecomputing and big-data storage. It has been presented a promising architecture,given estimates for its search performance, weight and power consumption, determined the typicalflight altitude, which provides the input data spatial resolution, which is necessary for objectorientedchange detection algorithms, based on a convolutional neural networks machine learning.It has been proposed organizational and technical solutions to speed up the data processingcycle, taking into account the requirements of the legislation regarding the declassification ofaerial survey data. The results obtained confirm that after the issuance of the Orion UAV by theFederal Air Transport Agency of the aircraft type certificate, which gives the right to performcommercial flights in the shared airspace of the Russian Federation, it will be possible to implementan aerial survey complex of high productivity and degree of autonomy using cut of the edgeCV & ML technologies. It seems the tactical, technical and economic capabilities of which proposedwill be orders of magnitude superior to the currently existing solutions especially for hardto-reach regions.
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