1,321 research outputs found
Small-Object Detection in Remote Sensing Images with End-to-End Edge-Enhanced GAN and Object Detector Network
The detection performance of small objects in remote sensing images is not
satisfactory compared to large objects, especially in low-resolution and noisy
images. A generative adversarial network (GAN)-based model called enhanced
super-resolution GAN (ESRGAN) shows remarkable image enhancement performance,
but reconstructed images miss high-frequency edge information. Therefore,
object detection performance degrades for small objects on recovered noisy and
low-resolution remote sensing images. Inspired by the success of edge enhanced
GAN (EEGAN) and ESRGAN, we apply a new edge-enhanced super-resolution GAN
(EESRGAN) to improve the image quality of remote sensing images and use
different detector networks in an end-to-end manner where detector loss is
backpropagated into the EESRGAN to improve the detection performance. We
propose an architecture with three components: ESRGAN, Edge Enhancement Network
(EEN), and Detection network. We use residual-in-residual dense blocks (RRDB)
for both the ESRGAN and EEN, and for the detector network, we use the faster
region-based convolutional network (FRCNN) (two-stage detector) and single-shot
multi-box detector (SSD) (one stage detector). Extensive experiments on a
public (car overhead with context) and a self-assembled (oil and gas storage
tank) satellite dataset show superior performance of our method compared to the
standalone state-of-the-art object detectors.Comment: This paper contains 27 pages and accepted for publication in MDPI
remote sensing journal. GitHub Repository:
https://github.com/Jakaria08/EESRGAN (Implementation
Crowd Counting with Decomposed Uncertainty
Research in neural networks in the field of computer vision has achieved
remarkable accuracy for point estimation. However, the uncertainty in the
estimation is rarely addressed. Uncertainty quantification accompanied by point
estimation can lead to a more informed decision, and even improve the
prediction quality. In this work, we focus on uncertainty estimation in the
domain of crowd counting. With increasing occurrences of heavily crowded events
such as political rallies, protests, concerts, etc., automated crowd analysis
is becoming an increasingly crucial task. The stakes can be very high in many
of these real-world applications. We propose a scalable neural network
framework with quantification of decomposed uncertainty using a bootstrap
ensemble. We demonstrate that the proposed uncertainty quantification method
provides additional insight to the crowd counting problem and is simple to
implement. We also show that our proposed method exhibits the state of the art
performances in many benchmark crowd counting datasets.Comment: Accepted in AAAI 2020 (Main Technical Track
An AI-Horticulture Monitoring and Prediction System with Automatic Object Counting
Estimating density maps and counting the number of objects of interest from images has a wide range of applications, such as crowd counting, traffic monitoring, cell microscopy in biomedical imaging, plant counting in agronomy, as well as environmental survey. Manual counting is a labor-intensive and time-consuming process. Over the past few years, the topic of automatic object counting by computers has been actively evolving from the classic machine learning methods based on handcrafted image features to end-to-end deep learning methods using data-driven feature engineering, for example by Convolutional Neural Networks (CNNs). In our research, we focus on the task of counting plants for large-scale nursery farms to build an AI-horticulture monitoring and prediction system using unmanned aerial vehicle (UAV) images. The common challenges of automatic object counting as other computer vision tasks are scenario difference, object occlusion, scale variation of views, non-uniform distribution, and perspective difference. For an AI-horticulture monitoring and prediction system for large-scale analysis, the plant species various a lot, so that the image features are different based on different appearance of species. In order to solve these complex problems, the deep convolutional neural network-based approaches are proposed.
Our method uses the density map as the ground truth to train the modified classic deep neural networks for object counting regression. Experiments are conducted comparing our proposed models with the state-of-the-art object counting and density estimation approaches. The results demonstrate that our proposed counting model outperforms state-of-the-art approaches by achieving the best counting performance with a mean absolute error of 1.93 and a mean square error of 2.68 on our horticulture nursery plant dataset
Bounding Box-Free Instance Segmentation Using Semi-Supervised Learning for Generating a City-Scale Vehicle Dataset
Vehicle classification is a hot computer vision topic, with studies ranging
from ground-view up to top-view imagery. In remote sensing, the usage of
top-view images allows for understanding city patterns, vehicle concentration,
traffic management, and others. However, there are some difficulties when
aiming for pixel-wise classification: (a) most vehicle classification studies
use object detection methods, and most publicly available datasets are designed
for this task, (b) creating instance segmentation datasets is laborious, and
(c) traditional instance segmentation methods underperform on this task since
the objects are small. Thus, the present research objectives are: (1) propose a
novel semi-supervised iterative learning approach using GIS software, (2)
propose a box-free instance segmentation approach, and (3) provide a city-scale
vehicle dataset. The iterative learning procedure considered: (1) label a small
number of vehicles, (2) train on those samples, (3) use the model to classify
the entire image, (4) convert the image prediction into a polygon shapefile,
(5) correct some areas with errors and include them in the training data, and
(6) repeat until results are satisfactory. To separate instances, we considered
vehicle interior and vehicle borders, and the DL model was the U-net with the
Efficient-net-B7 backbone. When removing the borders, the vehicle interior
becomes isolated, allowing for unique object identification. To recover the
deleted 1-pixel borders, we proposed a simple method to expand each prediction.
The results show better pixel-wise metrics when compared to the Mask-RCNN (82%
against 67% in IoU). On per-object analysis, the overall accuracy, precision,
and recall were greater than 90%. This pipeline applies to any remote sensing
target, being very efficient for segmentation and generating datasets.Comment: 38 pages, 10 figures, submitted to journa
DOTA: A Large-scale Dataset for Object Detection in Aerial Images
Object detection is an important and challenging problem in computer vision.
Although the past decade has witnessed major advances in object detection in
natural scenes, such successes have been slow to aerial imagery, not only
because of the huge variation in the scale, orientation and shape of the object
instances on the earth's surface, but also due to the scarcity of
well-annotated datasets of objects in aerial scenes. To advance object
detection research in Earth Vision, also known as Earth Observation and Remote
Sensing, we introduce a large-scale Dataset for Object deTection in Aerial
images (DOTA). To this end, we collect aerial images from different
sensors and platforms. Each image is of the size about 4000-by-4000 pixels and
contains objects exhibiting a wide variety of scales, orientations, and shapes.
These DOTA images are then annotated by experts in aerial image interpretation
using common object categories. The fully annotated DOTA images contains
instances, each of which is labeled by an arbitrary (8 d.o.f.)
quadrilateral To build a baseline for object detection in Earth Vision, we
evaluate state-of-the-art object detection algorithms on DOTA. Experiments
demonstrate that DOTA well represents real Earth Vision applications and are
quite challenging.Comment: Accepted to CVPR 201
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