Real-time scene understanding for UAV imagery based on deep convolutional neural networks

摘要

Real-time scene understanding is important for many applications of Unmanned Aerial Vehicles (UAVs) such as reconnaissance, surveillance, mapping, and infrastructure inspection. With the recent growth of computation power, it is feasible to use Deep Learning for real-time applications. Deep Convolutional Neural Networks (CNNs) have emerged as a powerful model for classifying image content, and are widely considered in the computer vision community to be the de facto standard approach for most problems. Current Deep learning approaches for image classification and object detection are designed and evaluated on lab setting human-centric photographs taken horizontally from a height of 1-2 meters. UAV images are taken vertically in high altitude; therefore the objects of interest are relatively small with a skewed vantage point which creates a real challenge in detection and classification of such images. Here we present a deep convolutional approach for classification of Aerial imagery taken by UAV. We applied our network on optical imagery taken with UAV RS-16 from Port Mansfield, TX. Experimental results in comparison with ground-truth show 93.6 % accuracy for UAV image classification.