618 research outputs found
Deep Learning-Based Object Detection in Maritime Unmanned Aerial Vehicle Imagery: Review and Experimental Comparisons
With the advancement of maritime unmanned aerial vehicles (UAVs) and deep
learning technologies, the application of UAV-based object detection has become
increasingly significant in the fields of maritime industry and ocean
engineering. Endowed with intelligent sensing capabilities, the maritime UAVs
enable effective and efficient maritime surveillance. To further promote the
development of maritime UAV-based object detection, this paper provides a
comprehensive review of challenges, relative methods, and UAV aerial datasets.
Specifically, in this work, we first briefly summarize four challenges for
object detection on maritime UAVs, i.e., object feature diversity, device
limitation, maritime environment variability, and dataset scarcity. We then
focus on computational methods to improve maritime UAV-based object detection
performance in terms of scale-aware, small object detection, view-aware,
rotated object detection, lightweight methods, and others. Next, we review the
UAV aerial image/video datasets and propose a maritime UAV aerial dataset named
MS2ship for ship detection. Furthermore, we conduct a series of experiments to
present the performance evaluation and robustness analysis of object detection
methods on maritime datasets. Eventually, we give the discussion and outlook on
future works for maritime UAV-based object detection. The MS2ship dataset is
available at
\href{https://github.com/zcj234/MS2ship}{https://github.com/zcj234/MS2ship}.Comment: 32 pages, 18 figure
FasterX: Real-Time Object Detection Based on Edge GPUs for UAV Applications
Real-time object detection on Unmanned Aerial Vehicles (UAVs) is a
challenging issue due to the limited computing resources of edge GPU devices as
Internet of Things (IoT) nodes. To solve this problem, in this paper, we
propose a novel lightweight deep learning architectures named FasterX based on
YOLOX model for real-time object detection on edge GPU. First, we design an
effective and lightweight PixSF head to replace the original head of YOLOX to
better detect small objects, which can be further embedded in the depthwise
separable convolution (DS Conv) to achieve a lighter head. Then, a slimmer
structure in the Neck layer termed as SlimFPN is developed to reduce parameters
of the network, which is a trade-off between accuracy and speed. Furthermore,
we embed attention module in the Head layer to improve the feature extraction
effect of the prediction head. Meanwhile, we also improve the label assignment
strategy and loss function to alleviate category imbalance and box optimization
problems of the UAV dataset. Finally, auxiliary heads are presented for online
distillation to improve the ability of position embedding and feature
extraction in PixSF head. The performance of our lightweight models are
validated experimentally on the NVIDIA Jetson NX and Jetson Nano GPU embedded
platforms.Extensive experiments show that FasterX models achieve better
trade-off between accuracy and latency on VisDrone2021 dataset compared to
state-of-the-art models.Comment: 12 pages, 7 figure
Application-aware optimization of Artificial Intelligence for deployment on resource constrained devices
Artificial intelligence (AI) is changing people's everyday life. AI techniques such as Deep Neural Networks (DNN) rely on heavy computational models, which are in principle designed to be executed on powerful HW platforms, such as desktop or server environments. However, the increasing need to apply such solutions in people's everyday life has encouraged the research for methods to allow their deployment on embedded, portable and stand-alone devices, such as mobile phones, which exhibit relatively low memory and computational resources. Such methods targets both the development of lightweight AI algorithms and their acceleration through dedicated HW.
This thesis focuses on the development of lightweight AI solutions, with attention to deep neural networks, to facilitate their deployment on resource constrained devices. Focusing on the computer vision field, we show how putting together the self learning ability of deep neural networks with application-specific knowledge, in the form of feature engineering, it is possible to dramatically reduce the total memory and computational burden, thus allowing the deployment on edge devices. The proposed approach aims to be complementary to already existing application-independent network compression solutions. In this work three main DNN optimization goals have been considered: increasing speed and accuracy, allowing training at the edge, and allowing execution on a microcontroller. For each of these we deployed the resulting algorithm to the target embedded device and measured its performance
Introduction to Drone Detection Radar with Emphasis on Automatic Target Recognition (ATR) technology
This paper discusses the challenges of detecting and categorizing small
drones with radar automatic target recognition (ATR) technology. The authors
suggest integrating ATR capabilities into drone detection radar systems to
improve performance and manage emerging threats. The study focuses primarily on
drones in Group 1 and 2. The paper highlights the need to consider kinetic
features and signal signatures, such as micro-Doppler, in ATR techniques to
efficiently recognize small drones. The authors also present a comprehensive
drone detection radar system design that balances detection and tracking
requirements, incorporating parameter adjustment based on scattering region
theory. They offer an example of a performance improvement achieved using
feedback and situational awareness mechanisms with the integrated ATR
capabilities. Furthermore, the paper examines challenges related to one-way
attack drones and explores the potential of cognitive radar as a solution. The
integration of ATR capabilities transforms a 3D radar system into a 4D radar
system, resulting in improved drone detection performance. These advancements
are useful in military, civilian, and commercial applications, and ongoing
research and development efforts are essential to keep radar systems effective
and ready to detect, track, and respond to emerging threats.Comment: 17 pages, 14 figures, submitted to a journal and being under revie
Real-time Embedded Person Detection and Tracking for Shopping Behaviour Analysis
Shopping behaviour analysis through counting and tracking of people in
shop-like environments offers valuable information for store operators and
provides key insights in the stores layout (e.g. frequently visited spots).
Instead of using extra staff for this, automated on-premise solutions are
preferred. These automated systems should be cost-effective, preferably on
lightweight embedded hardware, work in very challenging situations (e.g.
handling occlusions) and preferably work real-time. We solve this challenge by
implementing a real-time TensorRT optimized YOLOv3-based pedestrian detector,
on a Jetson TX2 hardware platform. By combining the detector with a sparse
optical flow tracker we assign a unique ID to each customer and tackle the
problem of loosing partially occluded customers. Our detector-tracker based
solution achieves an average precision of 81.59% at a processing speed of 10
FPS. Besides valuable statistics, heat maps of frequently visited spots are
extracted and used as an overlay on the video stream
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