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.

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.

In the Machine Learning era, Deep Neural Networks (DNNs) have taken the spotlight, due to their unmatchable performance in several applications, such as image processing, computer vision, and natural language processing. However, as DNNs grow in their complexity, their associated energy consumption becomes a challenging problem. Such challenge heightens for edge computing, where the computing devices are resource-constrained while operating on limited energy budget. Therefore, specialized optimizations for deep learning have to be performed at both software and hardware levels. In this paper, we comprehensively survey the current trends of such optimizations and discuss key open research mid-term and long-term challenges.

Many applications utilizing Unmanned Aerial Vehicles (UAVs) require the use of computer vision algorithms to analyze the information captured from their on-board camera. Recent advances in deep learning have made it possible to use single-shot Convolutional Neural Network (CNN) detection algorithms that process the input image to detect various objects of interest. To keep the computational demands low these neural networks typically operate on small image sizes which, however, makes it di cult to detect small objects. is is further emphasized when considering UAVs equipped with cameras where due to the viewing range, objects tend to appear relatively small. is paper therefore, explores the trade-o s involved when maintaining the resolution of the objects of interest by extracting smaller patches (tiles) from the larger input image and processing them using a neural network. Speci cally, we introduce an a ention mechanism to focus on detecting objects only in some of the tiles and a memory mechanism to keep track of information for tiles that are not processed. rough the analysis of di erent methods and experiments we show that by carefully selecting which tiles to process we can considerably improve the detection accuracy while maintaining comparable performance to CNNs that resize and process a single image which makes the proposed approach suitable for UAV applications.

No abstract

Unmanned Remotely Operated Vehicles (ROVs) are widely used in safety‐critical missions, spanning across monitoring, surveillance, and search and rescue missions. In most cases, the human operator controls the ROV under stressful conditions and in harsh environments. The physical and mental states of the operator are vital as they may cause involuntary movements, issuing unintentional commands due to fatigue or stress. Consequently, a joint monitoring mechanism that will assess the operator's commands can be beneficial in preventing potentially catastrophic events, such as accidents. To this end, in this study, we propose a data collection framework for creating a fused dataset, consisting of data obtained from both the human operator and the ROV, and we integrate stress induction to stimulate stress and fatigue that may affect operators during missions. We optimise the dataset, which includes the operator's physiological signals and inertial measurement unit (IMU) sensor data from the ROV, using data fusion approaches and feature extraction. We evaluate the dataset through the statistical analysis, illustrating a statistical difference between data characterising normal and involuntary commands. Finally, we evaluate the optimised and original datasets over a variety of classifiers, demonstrating an accuracy of 94% and 95%, respectively.

Machine learning methods comparison for esophagus tissue classification using in vivo OCT images. A neural network based approach discriminated better normal tissue from Barrett's Esophagus and dysplasia using various features with an accuracy of 87%.

No abstract