Object recognition for UAV navigation in complex environment

V. Knyaz; V. Kniaz

doi:10.1117/12.2574078

21 September 2020 Object recognition for UAV navigation in complex environment

V. Knyaz, V. Kniaz

Proceedings Volume 11533, Image and Signal Processing for Remote Sensing XXVI; 115330P (2020) https://doi.org/10.1117/12.2574078
Event: SPIE Remote Sensing, 2020, Online Only

Abstract

Impressive progress in technical characteristics of modern unmanned aerial vehicles (UAV) provides new opportunities for their exploiting in different applications and missions which were impossible earlier. The growing applicability of UAVs is based on high performance of modern computers and latest advances in sensor data processing techniques. Recent decades modern convolutional neural network (CNN) models have demonstrated the state of the art performance in many computer vision problems seemed to be solved properly only by a human. The study is aimed at developing a deep learning techniques for UAV autonomous navigation in complex environment in obstacle avoidance mode. Such kind of navigation is required for cargo delivery or rescue mission in urban, industrial or forestry environment when global geo-positioning system can be unavailable. For navigating in complex environment UAV have to recognize objects of observed scene and to estimate distance for possible obstacle. The proposed technique to solve these tasks exploits deep learning approach for image segmentation and depth map estimation using an image of the observed scene. The convolutional neural network model is developed capable to predict depth map of the observed scene along with scene segmentation according the predefined object classes. The proposed neural network architecture is based on generative adversarial model with generative part translating an input color image into an output voxel model. The aim of the discriminative part is to estimate how close the output to real data and to penalize false output. Both generative and discriminative parts are trained simultaneously on the specially prepared dataset. Evaluation on the testing part of the prepared dataset has demonstrated the ability of the developed neural network model to perform segmentation of unobserved complex scenes containing several objects and estimating depth map for this scene. The proposed neural network architecture provides high generalization ability for new scenes.

Conference Presentation

Citation Download Citation

V. Knyaz and V. Kniaz "Object recognition for UAV navigation in complex environment", Proc. SPIE 11533, Image and Signal Processing for Remote Sensing XXVI, 115330P (21 September 2020); https://doi.org/10.1117/12.2574078

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