Automated Military Vehicle Detection from Low-Altitude Aerial Images

Abstract

Detection and identification of military vehicles from aerial images is of great practical interest particularly for defense sector as it aids in predicting enemys move and hence, build early precautionary measures. Although due to advancement in the domain of self-driving cars, a vast literature of published algorithms exists that use the terrestrial data to solve the problem of vehicle detection in natural scenes. Directly translating these algorithms towards detection of both military and non-military vehicles in aerial images is not straight forward owing to high variability in scale, illumination and orientation together with articulations both in shape and structure. Moreover, unlike availability of terrestrial benchmark datasets such as Baidu Research Open-Access Dataset etc., there does not exist well-annotated datasets encompassing both military and non-military vehicles in aerial images which as a consequence limit the applicability of the state-of-the-art deep learning based object detection algorithms that have shown great success in the recent years. To this end, we have prepared a dataset of low-altitude aerial images that comprises of both real data (taken from military shows videos) and toy data (downloaded from YouTube videos). The dataset has been categorized into three main types, i.e., military vehicle, non-military vehicle and other non-vehicular objects. In total, there are 15,086 (11,733 toy and 3,353 real) vehicle images exhibiting a variety of different shapes, scales and orientations. To analyze the adequacy of the prepared dataset, we employed the state-of-the-art object detection algorithms to distinguish military and non-military vehicles. The experimental results show that the training of deep architectures using the customized/prepared dataset allows to recognize seven types of military and four types of non-military vehicles.