8,293 research outputs found
A 64mW DNN-based Visual Navigation Engine for Autonomous Nano-Drones
Fully-autonomous miniaturized robots (e.g., drones), with artificial
intelligence (AI) based visual navigation capabilities are extremely
challenging drivers of Internet-of-Things edge intelligence capabilities.
Visual navigation based on AI approaches, such as deep neural networks (DNNs)
are becoming pervasive for standard-size drones, but are considered out of
reach for nanodrones with size of a few cm. In this work, we
present the first (to the best of our knowledge) demonstration of a navigation
engine for autonomous nano-drones capable of closed-loop end-to-end DNN-based
visual navigation. To achieve this goal we developed a complete methodology for
parallel execution of complex DNNs directly on-bard of resource-constrained
milliwatt-scale nodes. Our system is based on GAP8, a novel parallel
ultra-low-power computing platform, and a 27 g commercial, open-source
CrazyFlie 2.0 nano-quadrotor. As part of our general methodology we discuss the
software mapping techniques that enable the state-of-the-art deep convolutional
neural network presented in [1] to be fully executed on-board within a strict 6
fps real-time constraint with no compromise in terms of flight results, while
all processing is done with only 64 mW on average. Our navigation engine is
flexible and can be used to span a wide performance range: at its peak
performance corner it achieves 18 fps while still consuming on average just
3.5% of the power envelope of the deployed nano-aircraft.Comment: 15 pages, 13 figures, 5 tables, 2 listings, accepted for publication
in the IEEE Internet of Things Journal (IEEE IOTJ
Deep learning in remote sensing: a review
Standing at the paradigm shift towards data-intensive science, machine
learning techniques are becoming increasingly important. In particular, as a
major breakthrough in the field, deep learning has proven as an extremely
powerful tool in many fields. Shall we embrace deep learning as the key to all?
Or, should we resist a 'black-box' solution? There are controversial opinions
in the remote sensing community. In this article, we analyze the challenges of
using deep learning for remote sensing data analysis, review the recent
advances, and provide resources to make deep learning in remote sensing
ridiculously simple to start with. More importantly, we advocate remote sensing
scientists to bring their expertise into deep learning, and use it as an
implicit general model to tackle unprecedented large-scale influential
challenges, such as climate change and urbanization.Comment: Accepted for publication IEEE Geoscience and Remote Sensing Magazin
Highly-Optimized Radar-Based Gesture Recognition System with Depthwise Expansion Module
The increasing integration of technology in our daily lives demands the development of
more convenient human–computer interaction (HCI) methods. Most of the current hand-based HCI
strategies exhibit various limitations, e.g., sensibility to variable lighting conditions and limitations
on the operating environment. Further, the deployment of such systems is often not performed
in resource-constrained contexts. Inspired by the MobileNetV1 deep learning network, this paper
presents a novel hand gesture recognition system based on frequency-modulated continuous wave
(FMCW) radar, exhibiting a higher recognition accuracy in comparison to the state-of-the-art systems.
First of all, the paper introduces a method to simplify radar preprocessing while preserving the main
information of the performed gestures. Then, a deep neural classifier with the novel Depthwise
Expansion Module based on the depthwise separable convolutions is presented. The introduced
classifier is optimized and deployed on the Coral Edge TPU board. The system defines and adopts
eight different hand gestures performed by five users, offering a classification accuracy of 98.13%
while operating in a low-power and resource-constrained environment.Electronic Components and Systems for European
Leadership Joint Undertaking under grant agreement No. 826655 (Tempo).European Union’s Horizon 2020 research and innovation programme and
Belgium, France, Germany, Switzerland, and the NetherlandsLodz University of Technology
Context-Patch Face Hallucination Based on Thresholding Locality-Constrained Representation and Reproducing Learning
Face hallucination is a technique that reconstruct high-resolution (HR) faces from low-resolution (LR) faces, by using the prior knowledge learned from HR/LR face pairs. Most state-of-the-arts leverage position-patch prior knowledge of human face to estimate the optimal representation coefficients for each image patch. However, they focus only the position information and usually ignore the context information of image patch. In addition, when they are confronted with misalignment or the Small Sample Size (SSS) problem, the hallucination performance is very poor. To this end, this study incorporates the contextual information of image patch and proposes a powerful and efficient context-patch based face hallucination approach, namely Thresholding Locality-constrained Representation and Reproducing learning (TLcR-RL). Under the context-patch based framework, we advance a thresholding based representation method to enhance the reconstruction accuracy and reduce the computational complexity. To further improve the performance of the proposed algorithm, we propose a promotion strategy called reproducing learning. By adding the estimated HR face to the training set, which can simulates the case that the HR version of the input LR face is present in the training set, thus iteratively enhancing the final hallucination result. Experiments demonstrate that the proposed TLcR-RL method achieves a substantial increase in the hallucinated results, both subjectively and objectively. Additionally, the proposed framework is more robust to face misalignment and the SSS problem, and its hallucinated HR face is still very good when the LR test face is from the real-world. The MATLAB source code is available at https://github.com/junjun-jiang/TLcR-RL
Ship recognition on the sea surface using aerial images taken by Uav : a deep learning approach
Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesOceans are very important for mankind, because they are a very important source of
food, they have a very large impact on the global environmental equilibrium, and it is
over the oceans that most of the world commerce is done. Thus, maritime surveillance
and monitoring, in particular identifying the ships used, is of great importance to
oversee activities like fishing, marine transportation, navigation in general, illegal
border encroachment, and search and rescue operations. In this thesis, we used images
obtained with Unmanned Aerial Vehicles (UAVs) over the Atlantic Ocean to identify
what type of ship (if any) is present in a given location. Images generated from UAV
cameras suffer from camera motion, scale variability, variability in the sea surface and
sun glares. Extracting information from these images is challenging and is mostly done
by human operators, but advances in computer vision technology and development of
deep learning techniques in recent years have made it possible to do so automatically.
We used four of the state-of-art pretrained deep learning network models, namely
VGG16, Xception, ResNet and InceptionResNet trained on ImageNet dataset, modified
their original structure using transfer learning based fine tuning techniques and then
trained them on our dataset to create new models. We managed to achieve very high
accuracy (99.6 to 99.9% correct classifications) when classifying the ships that appear
on the images of our dataset. With such a high success rate (albeit at the cost of high
computing power), we can proceed to implement these algorithms on maritime patrol
UAVs, and thus improve Maritime Situational Awareness
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