937 research outputs found
Synthetic Aperture Radar (SAR) Meets Deep Learning
This reprint focuses on the application of the combination of synthetic aperture radars and depth learning technology. It aims to further promote the development of SAR image intelligent interpretation technology. A synthetic aperture radar (SAR) is an important active microwave imaging sensor, whose all-day and all-weather working capacity give it an important place in the remote sensing community. Since the United States launched the first SAR satellite, SAR has received much attention in the remote sensing community, e.g., in geological exploration, topographic mapping, disaster forecast, and traffic monitoring. It is valuable and meaningful, therefore, to study SAR-based remote sensing applications. In recent years, deep learning represented by convolution neural networks has promoted significant progress in the computer vision community, e.g., in face recognition, the driverless field and Internet of things (IoT). Deep learning can enable computational models with multiple processing layers to learn data representations with multiple-level abstractions. This can greatly improve the performance of various applications. This reprint provides a platform for researchers to handle the above significant challenges and present their innovative and cutting-edge research results when applying deep learning to SAR in various manuscript types, e.g., articles, letters, reviews and technical reports
Object Detection in 20 Years: A Survey
Object detection, as of one the most fundamental and challenging problems in
computer vision, has received great attention in recent years. Its development
in the past two decades can be regarded as an epitome of computer vision
history. If we think of today's object detection as a technical aesthetics
under the power of deep learning, then turning back the clock 20 years we would
witness the wisdom of cold weapon era. This paper extensively reviews 400+
papers of object detection in the light of its technical evolution, spanning
over a quarter-century's time (from the 1990s to 2019). A number of topics have
been covered in this paper, including the milestone detectors in history,
detection datasets, metrics, fundamental building blocks of the detection
system, speed up techniques, and the recent state of the art detection methods.
This paper also reviews some important detection applications, such as
pedestrian detection, face detection, text detection, etc, and makes an in-deep
analysis of their challenges as well as technical improvements in recent years.Comment: This work has been submitted to the IEEE TPAMI for possible
publicatio
LO-Det: Lightweight Oriented Object Detection in Remote Sensing Images
A few lightweight convolutional neural network (CNN) models have been
recently designed for remote sensing object detection (RSOD). However, most of
them simply replace vanilla convolutions with stacked separable convolutions,
which may not be efficient due to a lot of precision losses and may not be able
to detect oriented bounding boxes (OBB). Also, the existing OBB detection
methods are difficult to constrain the shape of objects predicted by CNNs
accurately. In this paper, we propose an effective lightweight oriented object
detector (LO-Det). Specifically, a channel separation-aggregation (CSA)
structure is designed to simplify the complexity of stacked separable
convolutions, and a dynamic receptive field (DRF) mechanism is developed to
maintain high accuracy by customizing the convolution kernel and its perception
range dynamically when reducing the network complexity. The CSA-DRF component
optimizes efficiency while maintaining high accuracy. Then, a diagonal support
constraint head (DSC-Head) component is designed to detect OBBs and constrain
their shapes more accurately and stably. Extensive experiments on public
datasets demonstrate that the proposed LO-Det can run very fast even on
embedded devices with the competitive accuracy of detecting oriented objects.Comment: 15 page
Oriented Object Detection in Optical Remote Sensing Images using Deep Learning: A Survey
Oriented object detection is one of the most fundamental and challenging
tasks in remote sensing, aiming at locating the oriented objects of numerous
predefined object categories. Recently, deep learning based methods have
achieved remarkable performance in detecting oriented objects in optical remote
sensing imagery. However, a thorough review of the literature in remote sensing
has not yet emerged. Therefore, we give a comprehensive survey of recent
advances and cover many aspects of oriented object detection, including problem
definition, commonly used datasets, evaluation protocols, detection frameworks,
oriented object representations, and feature representations. Besides, the
state-of-the-art methods are analyzed and discussed. We finally discuss future
research directions to put forward some useful research guidance. We believe
that this survey shall be valuable to researchers across academia and industr
R3Det: Refined Single-Stage Detector with Feature Refinement for Rotating Object
Rotation detection is a challenging task due to the difficulties of locating
the multi-angle objects and separating them effectively from the background.
Though considerable progress has been made, for practical settings, there still
exist challenges for rotating objects with large aspect ratio, dense
distribution and category extremely imbalance. In this paper, we propose an
end-to-end refined single-stage rotation detector for fast and accurate object
detection by using a progressive regression approach from coarse to fine
granularity. Considering the shortcoming of feature misalignment in existing
refined single-stage detector, we design a feature refinement module to improve
detection performance by getting more accurate features. The key idea of
feature refinement module is to re-encode the position information of the
current refined bounding box to the corresponding feature points through
pixel-wise feature interpolation to realize feature reconstruction and
alignment. For more accurate rotation estimation, an approximate SkewIoU loss
is proposed to solve the problem that the calculation of SkewIoU is not
derivable. Experiments on three popular remote sensing public datasets DOTA,
HRSC2016, UCAS-AOD as well as one scene text dataset ICDAR2015 show the
effectiveness of our approach. Tensorflow and Pytorch version codes are
available at https://github.com/Thinklab-SJTU/R3Det_Tensorflow and
https://github.com/SJTU-Thinklab-Det/r3det-on-mmdetection, and R3Det is also
integrated in our open source rotation detection benchmark:
https://github.com/yangxue0827/RotationDetection.Comment: 13 pages, 12 figures, 9 table
Remote Sensing Object Detection Meets Deep Learning: A Meta-review of Challenges and Advances
Remote sensing object detection (RSOD), one of the most fundamental and
challenging tasks in the remote sensing field, has received longstanding
attention. In recent years, deep learning techniques have demonstrated robust
feature representation capabilities and led to a big leap in the development of
RSOD techniques. In this era of rapid technical evolution, this review aims to
present a comprehensive review of the recent achievements in deep learning
based RSOD methods. More than 300 papers are covered in this review. We
identify five main challenges in RSOD, including multi-scale object detection,
rotated object detection, weak object detection, tiny object detection, and
object detection with limited supervision, and systematically review the
corresponding methods developed in a hierarchical division manner. We also
review the widely used benchmark datasets and evaluation metrics within the
field of RSOD, as well as the application scenarios for RSOD. Future research
directions are provided for further promoting the research in RSOD.Comment: Accepted with IEEE Geoscience and Remote Sensing Magazine. More than
300 papers relevant to the RSOD filed were reviewed in this surve
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