New technologies for UAV navigation with real-time pattern recognition

Abstract

The study is dedicated to the investigation and analysis of real-time navigation technologies for UAVs utilizing image scanning and object recognition. This research employs the DJI Inspire 2 drone alongside MapBotix software, integrating adapted algorithms such as Faster R-CNN and MaskRCNN. During the flight of the DJI Inspire 2 drone over the Banska Bystrica region, information concerning object recognition and change detection within the studied area was collected. A 40-second scanning process documented two alterations, one of which occurred at the 10th second. Subsequent stages of image recognition were executed utilizing scaling techniques, thus ensuring the accuracy of data extracted from captured images. This research, in particular, pioneers the exploration of real-time UAV navigation technologies integrated with image recognition. The application of a customized MaskRCNN algorithm, comprising 9 convolutional layers, 4 max-pooling layers, and 1 detection layer, for analyzing scanned object images signifies a novel approach in this domain.

Keywords

• Unmanned aerial vehicle
• Photogrammetry
• DJI Inspire 2
• Orthophoto
• MaskRCNN, pattern recognition
• Change identification