Application of deep learning to the problem of vehicle detection in UAV images
Georgy V. Konoplich, Eugene Lane, Andrey A. Filchenkov · 2016
Detecting small objects such as vehicles in aerial images is a complicated problem, because it is difficult or impossible to find a suitable feature space to solve the problem for small objects. The aim of this work is to develop a system capable in real-time to solve the challenge of detection the vehicles. Deep convolutional networks can automatically extract rich features from the training sample and achieve good performance on a variety of data. In this paper, we present the adapted hybrid neural network (HDNN) in which the last layers are divided into several blocks of variable size so that the network could extract features of different scales. Experimental results show that HDNN, which was offered, exceeds the results of other conventional transport methods of detection.