6,712 research outputs found
VSSA-NET: Vertical Spatial Sequence Attention Network for Traffic Sign Detection
Although traffic sign detection has been studied for years and great progress
has been made with the rise of deep learning technique, there are still many
problems remaining to be addressed. For complicated real-world traffic scenes,
there are two main challenges. Firstly, traffic signs are usually small size
objects, which makes it more difficult to detect than large ones; Secondly, it
is hard to distinguish false targets which resemble real traffic signs in
complex street scenes without context information. To handle these problems, we
propose a novel end-to-end deep learning method for traffic sign detection in
complex environments. Our contributions are as follows: 1) We propose a
multi-resolution feature fusion network architecture which exploits densely
connected deconvolution layers with skip connections, and can learn more
effective features for the small size object; 2) We frame the traffic sign
detection as a spatial sequence classification and regression task, and propose
a vertical spatial sequence attention (VSSA) module to gain more context
information for better detection performance. To comprehensively evaluate the
proposed method, we do experiments on several traffic sign datasets as well as
the general object detection dataset and the results have shown the
effectiveness of our proposed method
Modeling Camera Effects to Improve Visual Learning from Synthetic Data
Recent work has focused on generating synthetic imagery to increase the size
and variability of training data for learning visual tasks in urban scenes.
This includes increasing the occurrence of occlusions or varying environmental
and weather effects. However, few have addressed modeling variation in the
sensor domain. Sensor effects can degrade real images, limiting
generalizability of network performance on visual tasks trained on synthetic
data and tested in real environments. This paper proposes an efficient,
automatic, physically-based augmentation pipeline to vary sensor effects
--chromatic aberration, blur, exposure, noise, and color cast-- for synthetic
imagery. In particular, this paper illustrates that augmenting synthetic
training datasets with the proposed pipeline reduces the domain gap between
synthetic and real domains for the task of object detection in urban driving
scenes
Pseudo-labels for Supervised Learning on Dynamic Vision Sensor Data, Applied to Object Detection under Ego-motion
In recent years, dynamic vision sensors (DVS), also known as event-based
cameras or neuromorphic sensors, have seen increased use due to various
advantages over conventional frame-based cameras. Using principles inspired by
the retina, its high temporal resolution overcomes motion blurring, its high
dynamic range overcomes extreme illumination conditions and its low power
consumption makes it ideal for embedded systems on platforms such as drones and
self-driving cars. However, event-based data sets are scarce and labels are
even rarer for tasks such as object detection. We transferred discriminative
knowledge from a state-of-the-art frame-based convolutional neural network
(CNN) to the event-based modality via intermediate pseudo-labels, which are
used as targets for supervised learning. We show, for the first time,
event-based car detection under ego-motion in a real environment at 100 frames
per second with a test average precision of 40.3% relative to our annotated
ground truth. The event-based car detector handles motion blur and poor
illumination conditions despite not explicitly trained to do so, and even
complements frame-based CNN detectors, suggesting that it has learnt
generalized visual representations
SSSDET: Simple Short and Shallow Network for Resource Efficient Vehicle Detection in Aerial Scenes
Detection of small-sized targets is of paramount importance in many aerial
vision-based applications. The commonly deployed low cost unmanned aerial
vehicles (UAVs) for aerial scene analysis are highly resource constrained in
nature. In this paper we propose a simple short and shallow network (SSSDet) to
robustly detect and classify small-sized vehicles in aerial scenes. The
proposed SSSDet is up to 4x faster, requires 4.4x less FLOPs, has 30x less
parameters, requires 31x less memory space and provides better accuracy in
comparison to existing state-of-the-art detectors. Thus, it is more suitable
for hardware implementation in real-time applications. We also created a new
airborne image dataset (ABD) by annotating 1396 new objects in 79 aerial images
for our experiments. The effectiveness of the proposed method is validated on
the existing VEDAI, DLR-3K, DOTA and Combined dataset. The SSSDet outperforms
state-of-the-art detectors in term of accuracy, speed, compute and memory
efficiency.Comment: International Conference on Image Processing (ICIP) 2019, Taipei,
Taiwa
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
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
Towards Large-Scale Small Object Detection: Survey and Benchmarks
With the rise of deep convolutional neural networks, object detection has
achieved prominent advances in past years. However, such prosperity could not
camouflage the unsatisfactory situation of Small Object Detection (SOD), one of
the notoriously challenging tasks in computer vision, owing to the poor visual
appearance and noisy representation caused by the intrinsic structure of small
targets. In addition, large-scale dataset for benchmarking small object
detection methods remains a bottleneck. In this paper, we first conduct a
thorough review of small object detection. Then, to catalyze the development of
SOD, we construct two large-scale Small Object Detection dAtasets (SODA),
SODA-D and SODA-A, which focus on the Driving and Aerial scenarios
respectively. SODA-D includes 24828 high-quality traffic images and 278433
instances of nine categories. For SODA-A, we harvest 2513 high resolution
aerial images and annotate 872069 instances over nine classes. The proposed
datasets, as we know, are the first-ever attempt to large-scale benchmarks with
a vast collection of exhaustively annotated instances tailored for
multi-category SOD. Finally, we evaluate the performance of mainstream methods
on SODA. We expect the released benchmarks could facilitate the development of
SOD and spawn more breakthroughs in this field. Datasets and codes are
available at: \url{https://shaunyuan22.github.io/SODA}
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