Deep-Learning Object Recognition in Multi-Spectral UAV imagery

The application area of unmanned aerial vehicles increases significantly recent years due to progress in hardware and algorithms for data acquisition and processing. Object detection and classification (recognition) in imagery acquired by unmanned aerial vehicle are the key tasks for many applications, and usually in practice an operator solves these tasks. Growing amount of data of different types and of different nature provides the possibility for deep machine learning which nowadays shows high level results for object detection and recognition. Two key problems are to be solved for applying deep learning for object recognition task when dealing with multi-spectral imagery: (a) availability of representative dataset for neural network training and testing and (b) effective way of multi-spectral data fusion during neural network training. The paper proposes the approaches for solving these problems. For creating a representative dataset synthetic infra-red images are generated using several real infra-red images and 3D model of a given object. An technique for realistic infra-red texturing based on accurate infra-red image exterior orientation and 3D model pose estimation is developed. It allows in automated mode to produce datasets of required volume for deep learning and automatically generate ground truth data for neural network training and testing. Two approaches for multi-spectral data fusion for object recognition are developed and evaluated: data level fusion and results level fusion. The results of the evaluation of both techniques on generated multi-spectral dataset are presented and discussed.