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 framesper-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.

Abstract-Cascade

Unmanned Aerial Vehicles (UAVs), equipped with camera sensors can facilitate enhanced situational awareness for many emergency response and disaster management applications since they are capable of operating in remote and difficult to access areas. In addition, by utilizing an embedded platform and deep learning UAVs can autonomously monitor a disaster stricken area, analyze the image in real-time and alert in the presence of various calamities such as collapsed buildings, flood, or fire in order to faster mitigate their effects on the environment and on human population. To this end, this paper focuses on the automated aerial scene classification of disaster events from on-board a UAV. Specifically, a dedicated Aerial Image Database for Emergency Response (AIDER) applications is introduced and a comparative analysis of existing approaches is performed. Through this analysis a lightweight convolutional neural network (CNN) architecture is developed, capable of running efficiently on an embedded platform achieving ∼ 3× higher performance compared to existing models with minimal memory requirements with less than 2% accuracy drop compared to the state-ofthe-art. These preliminary results provide a solid basis for further experimentation towards real-time aerial image classification for emergency response applications using UAVs.

The number of connected IoT devices is expected to reach over 20 billion by 2020. These range from basic sensor nodes that log and report the data for cloud processing, to the ones on the edge, that are capable of processing and analyzing the incoming information and taking an action accordingly. Machine learning, and in particular deep learning, is the defacto processing paradigm for intelligently processing these immense volumes of data. However, the resource inhibited environment of edge devices, owing to their limited energy budget, and low compute capabilities, render them a challenging platform for deployment of desired data analytics, particularly in realtime applications. In this paper therefore, we argue that for a wide range of emerging applications edge intelligence is a necessary evolutionary need, and thus we provide a summary of the challenges and opportunities that arise from this need. We showcase through a case study regarding computer vision for commercial drones, how these opportunities can be taken advantage, and how some of the challenges can be potentially addressed.

Real-time object detection is becoming necessary for a wide number of applications related to computer vision and image processing, security, bioinformatics, and several other areas. Existing software implementations of object detection algorithms are constrained in small-sized images and rely on favorable conditions in the image frame to achieve real-time detection frame rates. Efforts to design hardware architectures have yielded encouraging results, yet are mostly directed towards a single application, targeting specific operating environments. Consequently, there is a need for hardware architectures capable of detecting several objects in large image frames, and which can be used under several object detection scenarios. In this work, we present a generic, flexible parallel architecture, which is suitable for all ranges of object detection applications and image sizes. The architecture implements the AdaBoost-based detection algorithm, which is considered one of the most efficient object detection algorithms. Through both FPGA emulation and largescale implementation, and RTL synthesis and simulation, we illustrate that the architecture can detect objects in large images (up to 1024x768 pixels) with frame rates that can vary between 64-139 fps for various applications and input image frame sizes.