LocateUAV: Unmanned Aerial Vehicle Location Estimation via Contextual Analysis in an IoT Environment

Object detection supported by unmanned aerial vehicles (UAVs) has generated significant interest in recent years including applications, such as surveillance, search for missing persons, traffic, and disaster management. Location awareness is a challenging task, particularly, the deployment of UAVs in a global positioning system (GPS) restricted environment or GPS sensor failure. To mitigate this problem, we propose LocateUAV, a novel location awareness framework, to detect UAV’s location by processing the data from the visual sensor in real time using a lightweight convolutional neural network (CNN). Assuming that the drone is in an IoT environment, first, the object detection technique is applied to detect the object of interest (OOI), namely, signboard. Subsequently, optical character recognition (OCR) is applied to extract useful contextual information. In the final step, the extracted information is forwarded to the map application programming interface (API) to locate the UAV. We also present a newly created data set for LocateUAV, which comprises challenging scenarios for context analysis. Moreover, we also compress an existing lightweight model up to 45 MB for efficient processing over UAV, which is 19.5% when compared with the size of the original model. Finally, an in-depth comparison of various trained and efficient object detection and OCR techniques is presented to facilitate future research on the development of flex drone that can extract information from the surroundings of a location in a GPS-restricted environment.

I. Introduction

Unmanned aerial vehicle (UAV) is a well-known and convenient system for collecting pictures from an incredibly low elevation. In comparison to their smaller sizes, UAVs have a high level of flexibility and thus, provide aerial applications in various domains, such as fire detection, traffic congestion or accidents, search, and rescue operations according to user requirements. Owing to recent innovations in deep neural networks (DNNs), particularly, convolutional neural networks (CNNs), the performance of CNN-based detectors has increased dramatically [1], [2]. Most deep learning (DL) models not only defeat human-centered visual systems in occluded environments and accurate object detection but also surpass conventional algorithms in detection accuracy.