7,366 research outputs found
Effective Use of Dilated Convolutions for Segmenting Small Object Instances in Remote Sensing Imagery
Thanks to recent advances in CNNs, solid improvements have been made in
semantic segmentation of high resolution remote sensing imagery. However, most
of the previous works have not fully taken into account the specific
difficulties that exist in remote sensing tasks. One of such difficulties is
that objects are small and crowded in remote sensing imagery. To tackle with
this challenging task we have proposed a novel architecture called local
feature extraction (LFE) module attached on top of dilated front-end module.
The LFE module is based on our findings that aggressively increasing dilation
factors fails to aggregate local features due to sparsity of the kernel, and
detrimental to small objects. The proposed LFE module solves this problem by
aggregating local features with decreasing dilation factor. We tested our
network on three remote sensing datasets and acquired remarkably good results
for all datasets especially for small objects
Unmanned Aerial Systems for Wildland and Forest Fires
Wildfires represent an important natural risk causing economic losses, human
death and important environmental damage. In recent years, we witness an
increase in fire intensity and frequency. Research has been conducted towards
the development of dedicated solutions for wildland and forest fire assistance
and fighting. Systems were proposed for the remote detection and tracking of
fires. These systems have shown improvements in the area of efficient data
collection and fire characterization within small scale environments. However,
wildfires cover large areas making some of the proposed ground-based systems
unsuitable for optimal coverage. To tackle this limitation, Unmanned Aerial
Systems (UAS) were proposed. UAS have proven to be useful due to their
maneuverability, allowing for the implementation of remote sensing, allocation
strategies and task planning. They can provide a low-cost alternative for the
prevention, detection and real-time support of firefighting. In this paper we
review previous work related to the use of UAS in wildfires. Onboard sensor
instruments, fire perception algorithms and coordination strategies are
considered. In addition, we present some of the recent frameworks proposing the
use of both aerial vehicles and Unmanned Ground Vehicles (UV) for a more
efficient wildland firefighting strategy at a larger scale.Comment: A recent published version of this paper is available at:
https://doi.org/10.3390/drones501001
Lifting GIS Maps into Strong Geometric Context for Scene Understanding
Contextual information can have a substantial impact on the performance of
visual tasks such as semantic segmentation, object detection, and geometric
estimation. Data stored in Geographic Information Systems (GIS) offers a rich
source of contextual information that has been largely untapped by computer
vision. We propose to leverage such information for scene understanding by
combining GIS resources with large sets of unorganized photographs using
Structure from Motion (SfM) techniques. We present a pipeline to quickly
generate strong 3D geometric priors from 2D GIS data using SfM models aligned
with minimal user input. Given an image resectioned against this model, we
generate robust predictions of depth, surface normals, and semantic labels. We
show that the precision of the predicted geometry is substantially more
accurate other single-image depth estimation methods. We then demonstrate the
utility of these contextual constraints for re-scoring pedestrian detections,
and use these GIS contextual features alongside object detection score maps to
improve a CRF-based semantic segmentation framework, boosting accuracy over
baseline models
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
Learning Aerial Image Segmentation from Online Maps
This study deals with semantic segmentation of high-resolution (aerial)
images where a semantic class label is assigned to each pixel via supervised
classification as a basis for automatic map generation. Recently, deep
convolutional neural networks (CNNs) have shown impressive performance and have
quickly become the de-facto standard for semantic segmentation, with the added
benefit that task-specific feature design is no longer necessary. However, a
major downside of deep learning methods is that they are extremely data-hungry,
thus aggravating the perennial bottleneck of supervised classification, to
obtain enough annotated training data. On the other hand, it has been observed
that they are rather robust against noise in the training labels. This opens up
the intriguing possibility to avoid annotating huge amounts of training data,
and instead train the classifier from existing legacy data or crowd-sourced
maps which can exhibit high levels of noise. The question addressed in this
paper is: can training with large-scale, publicly available labels replace a
substantial part of the manual labeling effort and still achieve sufficient
performance? Such data will inevitably contain a significant portion of errors,
but in return virtually unlimited quantities of it are available in larger
parts of the world. We adapt a state-of-the-art CNN architecture for semantic
segmentation of buildings and roads in aerial images, and compare its
performance when using different training data sets, ranging from manually
labeled, pixel-accurate ground truth of the same city to automatic training
data derived from OpenStreetMap data from distant locations. We report our
results that indicate that satisfying performance can be obtained with
significantly less manual annotation effort, by exploiting noisy large-scale
training data.Comment: Published in IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSIN
DroTrack: High-speed Drone-based Object Tracking Under Uncertainty
We present DroTrack, a high-speed visual single-object tracking framework for
drone-captured video sequences. Most of the existing object tracking methods
are designed to tackle well-known challenges, such as occlusion and cluttered
backgrounds. The complex motion of drones, i.e., multiple degrees of freedom in
three-dimensional space, causes high uncertainty. The uncertainty problem leads
to inaccurate location predictions and fuzziness in scale estimations. DroTrack
solves such issues by discovering the dependency between object representation
and motion geometry. We implement an effective object segmentation based on
Fuzzy C Means (FCM). We incorporate the spatial information into the membership
function to cluster the most discriminative segments. We then enhance the
object segmentation by using a pre-trained Convolution Neural Network (CNN)
model. DroTrack also leverages the geometrical angular motion to estimate a
reliable object scale. We discuss the experimental results and performance
evaluation using two datasets of 51,462 drone-captured frames. The combination
of the FCM segmentation and the angular scaling increased DroTrack precision by
up to and decreased the centre location error by pixels on average.
DroTrack outperforms all the high-speed trackers and achieves comparable
results in comparison to deep learning trackers. DroTrack offers high frame
rates up to 1000 frame per second (fps) with the best location precision, more
than a set of state-of-the-art real-time trackers.Comment: 10 pages, 12 figures, FUZZ-IEEE 202
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