137 research outputs found
Fusion of Multispectral Data Through Illumination-aware Deep Neural Networks for Pedestrian Detection
Multispectral pedestrian detection has received extensive attention in recent
years as a promising solution to facilitate robust human target detection for
around-the-clock applications (e.g. security surveillance and autonomous
driving). In this paper, we demonstrate illumination information encoded in
multispectral images can be utilized to significantly boost performance of
pedestrian detection. A novel illumination-aware weighting mechanism is present
to accurately depict illumination condition of a scene. Such illumination
information is incorporated into two-stream deep convolutional neural networks
to learn multispectral human-related features under different illumination
conditions (daytime and nighttime). Moreover, we utilized illumination
information together with multispectral data to generate more accurate semantic
segmentation which are used to boost pedestrian detection accuracy. Putting all
of the pieces together, we present a powerful framework for multispectral
pedestrian detection based on multi-task learning of illumination-aware
pedestrian detection and semantic segmentation. Our proposed method is trained
end-to-end using a well-designed multi-task loss function and outperforms
state-of-the-art approaches on KAIST multispectral pedestrian dataset
Instance segmentation of standing dead trees in dense forest from aerial imagery using deep learning
"© 2022 The Author(s). Published by Elsevier B.V. on behalf of International Society of Photogrammetry and Remote Sensing (isprs). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)"Mapping standing dead trees, especially, in natural forests is very important for evaluation of the forest's health status, and its capability for storing Carbon, and the conservation of biodiversity. Apparently, natural forests have larger areas which renders the classical field surveying method very challenging, time-consuming, labor-intensive, and unsustainable. Thus, for effective forest management, there is the need for an automated approach that would be cost-effective. With the advent of Machine Learning, Deep Learning has proven to successfully achieve excellent results. This study presents an adjusted Mask R-CNN Deep Learning approach for detecting and segmenting standing dead trees in a mixed dense forest from CIR aerial imagery using a limited (195 images) training dataset. First, transfer learning is considered coupled with the image augmentation technique to leverage the limitation of training datasets. Then, we strategically selected hyperparameters to suit appropriately our model's architecture that fits well with our type of data (dead trees in images). Finally, to assess the generalization capability of our model's performance, a test dataset that was not confronted to the deep neural network was used for comprehensive evaluation. Our model recorded promising results reaching a mean average precision, average recall, and average F1-Score of 0.85, 0.88, and 0.87 respectively, despite our relatively low resolution (20 cm) dataset. Consequently, our model could be used for automation in standing dead tree detection and segmentation for enhanced forest management. This is equally significant for biodiversity conservation, and forest Carbon storage estimation.publishedVersio
Ultrafast Radiographic Imaging and Tracking: An overview of instruments, methods, data, and applications
Ultrafast radiographic imaging and tracking (U-RadIT) use state-of-the-art
ionizing particle and light sources to experimentally study sub-nanosecond
dynamic processes in physics, chemistry, biology, geology, materials science
and other fields. These processes, fundamental to nuclear fusion energy,
advanced manufacturing, green transportation and others, often involve one mole
or more atoms, and thus are challenging to compute by using the first
principles of quantum physics or other forward models. One of the central
problems in U-RadIT is to optimize information yield through, e.g.
high-luminosity X-ray and particle sources, efficient imaging and tracking
detectors, novel methods to collect data, and large-bandwidth online and
offline data processing, regulated by the underlying physics, statistics, and
computing power. We review and highlight recent progress in: a.) Detectors; b.)
U-RadIT modalities; c.) Data and algorithms; and d.) Applications.
Hardware-centric approaches to U-RadIT optimization are constrained by detector
material properties, low signal-to-noise ratio, high cost and long development
cycles of critical hardware components such as ASICs. Interpretation of
experimental data, including comparisons with forward models, is frequently
hindered by sparse measurements, model and measurement uncertainties, and
noise. Alternatively, U-RadIT makes increasing use of data science and machine
learning algorithms, including experimental implementations of compressed
sensing. Machine learning and artificial intelligence approaches, refined by
physics and materials information, may also contribute significantly to data
interpretation, uncertainty quantification and U-RadIT optimization.Comment: 51 pages, 31 figures; Overview of ultrafast radiographic imaging and
tracking as a part of ULITIMA 2023 conference, Mar. 13-16,2023, Menlo Park,
CA, US
Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directions
Breast cancer has reached the highest incidence rate worldwide among all
malignancies since 2020. Breast imaging plays a significant role in early
diagnosis and intervention to improve the outcome of breast cancer patients. In
the past decade, deep learning has shown remarkable progress in breast cancer
imaging analysis, holding great promise in interpreting the rich information
and complex context of breast imaging modalities. Considering the rapid
improvement in the deep learning technology and the increasing severity of
breast cancer, it is critical to summarize past progress and identify future
challenges to be addressed. In this paper, we provide an extensive survey of
deep learning-based breast cancer imaging research, covering studies on
mammogram, ultrasound, magnetic resonance imaging, and digital pathology images
over the past decade. The major deep learning methods, publicly available
datasets, and applications on imaging-based screening, diagnosis, treatment
response prediction, and prognosis are described in detail. Drawn from the
findings of this survey, we present a comprehensive discussion of the
challenges and potential avenues for future research in deep learning-based
breast cancer imaging.Comment: Survey, 41 page
Multimodal perception for autonomous driving
Mención Internacional en el tÃtulo de doctorAutonomous driving is set to play an important role among intelligent
transportation systems in the coming decades. The advantages
of its large-scale implementation –reduced accidents, shorter commuting
times, or higher fuel efficiency– have made its development a priority
for academia and industry. However, there is still a long way to
go to achieve full self-driving vehicles, capable of dealing with any
scenario without human intervention. To this end, advances in control,
navigation and, especially, environment perception technologies
are yet required. In particular, the detection of other road users that
may interfere with the vehicle’s trajectory is a key element, since it
allows to model the current traffic situation and, thus, to make decisions
accordingly.
The objective of this thesis is to provide solutions to some of
the main challenges of on-board perception systems, such as extrinsic
calibration of sensors, object detection, and deployment on
real platforms. First, a calibration method for obtaining the relative
transformation between pairs of sensors is introduced, eliminating
the complex manual adjustment of these parameters. The algorithm
makes use of an original calibration pattern and supports LiDARs,
and monocular and stereo cameras. Second, different deep learning
models for 3D object detection using LiDAR data in its bird’s eye
view projection are presented. Through a novel encoding, the use
of architectures tailored to image detection is proposed to process
the 3D information of point clouds in real time. Furthermore, the
effectiveness of using this projection together with image features is
analyzed. Finally, a method to mitigate the accuracy drop of LiDARbased
detection networks when deployed in ad-hoc configurations is
introduced. For this purpose, the simulation of virtual signals mimicking
the specifications of the desired real device is used to generate
new annotated datasets that can be used to train the models.
The performance of the proposed methods is evaluated against
other existing alternatives using reference benchmarks in the field of
computer vision (KITTI and nuScenes) and through experiments in
open traffic with an automated vehicle. The results obtained demonstrate
the relevance of the presented work and its suitability for commercial
use.La conducción autónoma está llamada a jugar un papel importante en
los sistemas inteligentes de transporte de las próximas décadas. Las
ventajas de su implementación a larga escala –disminución de accidentes,
reducción del tiempo de trayecto, u optimización del consumo–
han convertido su desarrollo en una prioridad para la academia y
la industria. Sin embargo, todavÃa hay un largo camino por delante
hasta alcanzar una automatización total, capaz de enfrentarse a cualquier
escenario sin intervención humana. Para ello, aún se requieren
avances en las tecnologÃas de control, navegación y, especialmente,
percepción del entorno. Concretamente, la detección de otros usuarios
de la carretera que puedan interferir en la trayectoria del vehÃculo
es una pieza fundamental para conseguirlo, puesto que permite modelar
el estado actual del tráfico y tomar decisiones en consecuencia.
El objetivo de esta tesis es aportar soluciones a algunos de los
principales retos de los sistemas de percepción embarcados, como
la calibración extrÃnseca de los sensores, la detección de objetos, y su
despliegue en plataformas reales. En primer lugar, se introduce un
método para la obtención de la transformación relativa entre pares
de sensores, eliminando el complejo ajuste manual de estos parámetros.
El algoritmo hace uso de un patrón de calibración propio y da
soporte a cámaras monoculares, estéreo, y LiDAR. En segundo lugar,
se presentan diferentes modelos de aprendizaje profundo para la detección
de objectos en 3D utilizando datos de escáneres LiDAR en su
proyección en vista de pájaro. A través de una nueva codificación, se
propone la utilización de arquitecturas de detección en imagen para
procesar en tiempo real la información tridimensional de las nubes
de puntos. Además, se analiza la efectividad del uso de esta proyección
junto con caracterÃsticas procedentes de imágenes. Por último,
se introduce un método para mitigar la pérdida de precisión de las
redes de detección basadas en LiDAR cuando son desplegadas en
configuraciones ad-hoc. Para ello, se plantea la simulación de señales
virtuales con las caracterÃsticas del modelo real que se quiere utilizar,
generando asà nuevos conjuntos anotados para entrenar los modelos.
El rendimiento de los métodos propuestos es evaluado frente a
otras alternativas existentes haciendo uso de bases de datos de referencia
en el campo de la visión por computador (KITTI y nuScenes),
y mediante experimentos en tráfico abierto empleando un vehÃculo
automatizado. Los resultados obtenidos demuestran la relevancia de
los trabajos presentados y su viabilidad para un uso comercial.Programa de Doctorado en IngenierÃa Eléctrica, Electrónica y Automática por la Universidad Carlos III de MadridPresidente: Jesús GarcÃa Herrero.- Secretario: Ignacio Parra Alonso.- Vocal: Gustavo Adolfo Peláez Coronad
Artificial Neural Networks and Evolutionary Computation in Remote Sensing
Artificial neural networks (ANNs) and evolutionary computation methods have been successfully applied in remote sensing applications since they offer unique advantages for the analysis of remotely-sensed images. ANNs are effective in finding underlying relationships and structures within multidimensional datasets. Thanks to new sensors, we have images with more spectral bands at higher spatial resolutions, which clearly recall big data problems. For this purpose, evolutionary algorithms become the best solution for analysis. This book includes eleven high-quality papers, selected after a careful reviewing process, addressing current remote sensing problems. In the chapters of the book, superstructural optimization was suggested for the optimal design of feedforward neural networks, CNN networks were deployed for a nanosatellite payload to select images eligible for transmission to ground, a new weight feature value convolutional neural network (WFCNN) was applied for fine remote sensing image segmentation and extracting improved land-use information, mask regional-convolutional neural networks (Mask R-CNN) was employed for extracting valley fill faces, state-of-the-art convolutional neural network (CNN)-based object detection models were applied to automatically detect airplanes and ships in VHR satellite images, a coarse-to-fine detection strategy was employed to detect ships at different sizes, and a deep quadruplet network (DQN) was proposed for hyperspectral image classification
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