977 research outputs found
Estimating Free-Flow Speed with LiDAR and Overhead Imagery
Understanding free-flow speed is fundamental to transportation engineering in order to improve traffic flow, control, and planning. The free-flow speed of a road segment is the average speed of automobiles unaffected by traffic congestion or delay. Collecting speed data across a state is both expensive and time consuming. Some approaches have been presented to estimate speed using geometric road features for certain types of roads in limited environments. However, estimating speed at state scale for varying landscapes, environments, and road qualities has been relegated to manual engineering and expensive sensor networks. This thesis proposes an automated approach for estimating free-flow speed using LiDAR (Light Detection and Ranging) point clouds and satellite imagery. Employing deep learning for high-level pattern recognition and feature extraction, we present methods for predicting free-flow speed across the state of Kentucky
Multi-Scale Hierarchical Conditional Random Field for Railway Electrification Scene Classification Using Mobile Laser Scanning Data
With the recent rapid development of high-speed railway in many countries, precise inspection for railway electrification systems has become more significant to ensure safe railway operation. However, this time-consuming manual inspection is not satisfactory for the high-demanding inspection task, thus a safe, fast and automatic inspection method is required. With LiDAR (Light Detection and Ranging) data becoming more available, the accurate railway electrification scene understanding using LiDAR data becomes feasible towards automatic 3D precise inspection.
This thesis presents a supervised learning method to classify railway electrification objects from Mobile Laser Scanning (MLS) data. First, a multi-range Conditional Random Field (CRF), which characterizes not only labeling homogeneity at a short range, but also the layout compatibility between different objects at a middle range in the probabilistic graphical model is implemented and tested. Then, this multi-range CRF model will be extended and improved into a hierarchical CRF model to consider multi-scale layout compatibility at full range. The proposed method is evaluated on a dataset collected in Korea with complex railway electrification systems environment. The experiment shows the effectiveness of proposed model
Toward Global Localization of Unmanned Aircraft Systems using Overhead Image Registration with Deep Learning Convolutional Neural Networks
Global localization, in which an unmanned aircraft system (UAS) estimates its unknown current location without access to its take-off location or other locational data from its flight path, is a challenging problem. This research brings together aspects from the remote sensing, geoinformatics, and machine learning disciplines by framing the global localization problem as a geospatial image registration problem in which overhead aerial and satellite imagery serve as a proxy for UAS imagery. A literature review is conducted covering the use of deep learning convolutional neural networks (DLCNN) with global localization and other related geospatial imagery applications. Differences between geospatial imagery taken from the overhead perspective and terrestrial imagery are discussed, as well as difficulties in using geospatial overhead imagery for image registration due to a lack of suitable machine learning datasets. Geospatial analysis is conducted to identify suitable areas for future UAS imagery collection. One of these areas, Jerusalem northeast (JNE) is selected as the area of interest (AOI) for this research. Multi-modal, multi-temporal, and multi-resolution geospatial overhead imagery is aggregated from a variety of publicly available sources and processed to create a controlled image dataset called Jerusalem northeast rural controlled imagery (JNE RCI). JNE RCI is tested with handcrafted feature-based methods SURF and SIFT and a non-handcrafted feature-based pre-trained fine-tuned VGG-16 DLCNN on coarse-grained image registration. Both handcrafted and non-handcrafted feature based methods had difficulty with the coarse-grained registration process. The format of JNE RCI is determined to be unsuitable for the coarse-grained registration process with DLCNNs and the process to create a new supervised machine learning dataset, Jerusalem northeast machine learning (JNE ML) is covered in detail. A multi-resolution grid based approach is used, where each grid cell ID is treated as the supervised training label for that respective resolution. Pre-trained fine-tuned VGG-16 DLCNNs, two custom architecture two-channel DLCNNs, and a custom chain DLCNN are trained on JNE ML for each spatial resolution of subimages in the dataset. All DLCNNs used could more accurately coarsely register the JNE ML subimages compared to the pre-trained fine-tuned VGG-16 DLCNN on JNE RCI. This shows the process for creating JNE ML is valid and is suitable for using machine learning with the coarse-grained registration problem. All custom architecture two-channel DLCNNs and the custom chain DLCNN were able to more accurately coarsely register the JNE ML subimages compared to the fine-tuned pre-trained VGG-16 approach. Both the two-channel custom DLCNNs and the chain DLCNN were able to generalize well to new imagery that these networks had not previously trained on. Through the contributions of this research, a foundation is laid for future work to be conducted on the UAS global localization problem within the rural forested JNE AOI
A New Wave in Robotics: Survey on Recent mmWave Radar Applications in Robotics
We survey the current state of millimeterwave (mmWave) radar applications in
robotics with a focus on unique capabilities, and discuss future opportunities
based on the state of the art. Frequency Modulated Continuous Wave (FMCW)
mmWave radars operating in the 76--81GHz range are an appealing alternative to
lidars, cameras and other sensors operating in the near visual spectrum. Radar
has been made more widely available in new packaging classes, more convenient
for robotics and its longer wavelengths have the ability to bypass visual
clutter such as fog, dust, and smoke. We begin by covering radar principles as
they relate to robotics. We then review the relevant new research across a
broad spectrum of robotics applications beginning with motion estimation,
localization, and mapping. We then cover object detection and classification,
and then close with an analysis of current datasets and calibration techniques
that provide entry points into radar research.Comment: 19 Pages, 11 Figures, 2 Tables, TRO Submission pendin
Automated forest inventory: analysis of high-density airborne LiDAR point clouds with 3D deep learning
Detailed forest inventories are critical for sustainable and flexible
management of forest resources, to conserve various ecosystem services. Modern
airborne laser scanners deliver high-density point clouds with great potential
for fine-scale forest inventory and analysis, but automatically partitioning
those point clouds into meaningful entities like individual trees or tree
components remains a challenge. The present study aims to fill this gap and
introduces a deep learning framework, termed ForAINet, that is able to perform
such a segmentation across diverse forest types and geographic regions. From
the segmented data, we then derive relevant biophysical parameters of
individual trees as well as stands. The system has been tested on FOR-Instance,
a dataset of point clouds that have been acquired in five different countries
using surveying drones. The segmentation back-end achieves over 85% F-score for
individual trees, respectively over 73% mean IoU across five semantic
categories: ground, low vegetation, stems, live branches and dead branches.
Building on the segmentation results our pipeline then densely calculates
biophysical features of each individual tree (height, crown diameter, crown
volume, DBH, and location) and properties per stand (digital terrain model and
stand density). Especially crown-related features are in most cases retrieved
with high accuracy, whereas the estimates for DBH and location are less
reliable, due to the airborne scanning setup
Vehicle classification in intelligent transport systems: an overview, methods and software perspective
Vehicle Classification (VC) is a key element of Intelligent Transportation Systems (ITS). Diverse ranges of ITS applications like security systems, surveillance frameworks, fleet monitoring, traffic safety, and automated parking are using VC. Basically, in the current VC methods, vehicles are classified locally as a vehicle passes through a monitoring area, by fixed sensors or using a compound method. This paper presents a pervasive study on the state of the art of VC methods. We introduce a detailed VC taxonomy and explore the different kinds of traffic information that can be extracted via each method. Subsequently, traditional and cutting edge VC systems are investigated from different aspects. Specifically, strengths and shortcomings of the existing VC methods are discussed and real-time alternatives like Vehicular Ad-hoc Networks (VANETs) are investigated to convey physical as well as kinematic characteristics of the vehicles. Finally, we review a broad range of soft computing solutions involved in VC in the context of machine learning, neural networks, miscellaneous features, models and other methods
Real-time implementation of 3D LiDAR point cloud semantic segmentation in an FPGA
Dissertação de mestrado em Informatics EngineeringIn the last few years, the automotive industry has relied heavily on deep learning applications for
perception solutions. With data-heavy sensors, such as LiDAR, becoming a standard, the task of
developing low-power and real-time applications has become increasingly more challenging. To obtain
the maximum computational efficiency, no longer can one focus solely on the software aspect of such
applications, while disregarding the underlying hardware.
In this thesis, a hardware-software co-design approach is used to implement an inference application
leveraging the SqueezeSegV3, a LiDAR-based convolutional neural network, on the Versal ACAP VCK190
FPGA. Automotive requirements carefully drive the development of the proposed solution, with real-time
performance and low power consumption being the target metrics.
A first experiment validates the suitability of Xilinx’s Vitis-AI tool for the deployment of deep
convolutional neural networks on FPGAs. Both the ResNet-18 and SqueezeNet neural networks are
deployed to the Zynq UltraScale+ MPSoC ZCU104 and Versal ACAP VCK190 FPGAs. The results show
that both networks achieve far more than the real-time requirements while consuming low power.
Compared to an NVIDIA RTX 3090 GPU, the performance per watt during both network’s inference is 12x
and 47.8x higher and 15.1x and 26.6x higher respectively for the Zynq UltraScale+ MPSoC ZCU104 and
the Versal ACAP VCK190 FPGA. These results are obtained with no drop in accuracy in the quantization
step.
A second experiment builds upon the results of the first by deploying a real-time application containing
the SqueezeSegV3 model using the Semantic-KITTI dataset. A framerate of 11 Hz is achieved with a peak
power consumption of 78 Watts. The quantization step results in a minimal accuracy and IoU degradation
of 0.7 and 1.5 points respectively. A smaller version of the same model is also deployed achieving a
framerate of 19 Hz and a peak power consumption of 76 Watts. The application performs semantic
segmentation over all the point cloud with a field of view of 360°.Nos últimos anos a indústria automóvel tem cada vez mais aplicado deep learning para solucionar
problemas de perceção. Dado que os sensores que produzem grandes quantidades de dados, como o
LiDAR, se têm tornado standard, a tarefa de desenvolver aplicações de baixo consumo energético e com
capacidades de reagir em tempo real tem-se tornado cada vez mais desafiante. Para obter a máxima
eficiência computacional, deixou de ser possÃvel focar-se apenas no software aquando do
desenvolvimento de uma aplicação deixando de lado o hardware subjacente.
Nesta tese, uma abordagem de desenvolvimento simultâneo de hardware e software é usada para
implementar uma aplicação de inferência usando o SqueezeSegV3, uma rede neuronal convolucional
profunda, na FPGA Versal ACAP VCK190. São os requisitos automotive que guiam o desenvolvimento da
solução proposta, sendo a performance em tempo real e o baixo consumo energético, as métricas alvo
principais.
Uma primeira experiência valida a aptidão da ferramenta Vitis-AI para a implantação de redes
neuronais convolucionais profundas em FPGAs. As redes ResNet-18 e SqueezeNet são ambas
implantadas nas FPGAs Zynq UltraScale+ MPSoC ZCU104 e Versal ACAP VCK190. Os resultados
mostram que ambas as redes ultrapassam os requisitos de tempo real consumindo pouca energia.
Comparado com a GPU NVIDIA RTX 3090, a performance por Watt durante a inferência de ambas as
redes é superior em 12x e 47.8x e 15.1x e 26.6x respetivamente na Zynq UltraScale+ MPSoC ZCU104
e na Versal ACAP VCK190. Estes resultados foram obtidos sem qualquer perda de accuracy na etapa de
quantização.
Uma segunda experiência é feita no seguimento dos resultados da primeira, implantando uma
aplicação de inferência em tempo real contendo o modelo SqueezeSegV3 e usando o conjunto de dados
Semantic-KITTI. Um framerate de 11 Hz é atingido com um pico de consumo energético de 78 Watts. O
processo de quantização resulta numa perda mÃnima de accuracy e IoU com valores de 0.7 e 1.5 pontos
respetivamente. Uma versão mais pequena do mesmo modelo é também implantada, atingindo uma
framerate de 19 Hz e um pico de consumo energético de 76 Watts. A aplicação desenvolvida executa
segmentação semântica sobre a totalidade das nuvens de pontos LiDAR, com um campo de visão de
360°
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