DroNet: Efficient convolutional neural network detector for real-time UAV applications

TL;DR

This paper introduces DroNet, a lightweight CNN architecture designed for real-time vehicle detection in UAVs, balancing accuracy and computational efficiency for resource-constrained embedded platforms.

Contribution

The paper presents a novel, optimized CNN architecture tailored for UAVs, including data collection, training, and deployment strategies for real-time vehicle detection.

Findings

Achieves ~95% detection accuracy

Operates at 5-18 FPS on embedded platforms

Suitable for deployment on commercial UAVs

Abstract

Unmanned Aerial Vehicles (drones) are emerging as a promising technology for both environmental and infrastructure monitoring, with broad use in a plethora of applications. Many such applications require the use of computer vision algorithms in order to analyse the information captured from an on-board camera. Such applications include detecting vehicles for emergency response and traffic monitoring. This paper therefore, explores the trade-offs involved in the development of a single-shot object detector based on deep convolutional neural networks (CNNs) that can enable UAVs to perform vehicle detection under a resource constrained environment such as in a UAV. The paper presents a holistic approach for designing such systems; the data collection and training stages, the CNN architecture, and the optimizations necessary to efficiently map such a CNN on a lightweight embedded processing platform suitable for deployment on UAVs. Through the analysis we propose a CNN architecture that is capable of detecting vehicles from aerial UAV images and can operate between 5-18 frames-per-second for a variety of platforms with an overall accuracy of ~95%. Overall, the proposed architecture is suitable for UAV applications, utilizing low-power embedded processors that can be deployed on commercial UAVs.