110,409 research outputs found
Recent advances in deep learning for object detection
Object detection is a fundamental visual recognition problem in computer
vision and has been widely studied in the past decades. Visual object detection
aims to find objects of certain target classes with precise localization in a
given image and assign each object instance a corresponding class label. Due to
the tremendous successes of deep learning based image classification, object
detection techniques using deep learning have been actively studied in recent
years. In this paper, we give a comprehensive survey of recent advances in
visual object detection with deep learning. By reviewing a large body of recent
related work in literature, we systematically analyze the existing object
detection frameworks and organize the survey into three major parts: (i)
detection components, (ii) learning strategies, and (iii) applications &
benchmarks. In the survey, we cover a variety of factors affecting the
detection performance in detail, such as detector architectures, feature
learning, proposal generation, sampling strategies, etc. Finally, we discuss
several future directions to facilitate and spur future research for visual
object detection with deep learning. Keywords: Object Detection, Deep Learning,
Deep Convolutional Neural Network
Deep learning for 3D Object Detection and Tracking in Autonomous Driving: A Brief Survey
Object detection and tracking are vital and fundamental tasks for autonomous
driving, aiming at identifying and locating objects from those predefined
categories in a scene. 3D point cloud learning has been attracting more and
more attention among all other forms of self-driving data. Currently, there are
many deep learning methods for 3D object detection. However, the tasks of
object detection and tracking for point clouds still need intensive study due
to the unique characteristics of point cloud data. To help get a good grasp of
the present situation of this research, this paper shows recent advances in
deep learning methods for 3D object detection and tracking.Comment: 12 pages, 8 figure
Deep Exemplar 2D-3D Detection by Adapting from Real to Rendered Views
This paper presents an end-to-end convolutional neural network (CNN) for
2D-3D exemplar detection. We demonstrate that the ability to adapt the features
of natural images to better align with those of CAD rendered views is critical
to the success of our technique. We show that the adaptation can be learned by
compositing rendered views of textured object models on natural images. Our
approach can be naturally incorporated into a CNN detection pipeline and
extends the accuracy and speed benefits from recent advances in deep learning
to 2D-3D exemplar detection. We applied our method to two tasks: instance
detection, where we evaluated on the IKEA dataset, and object category
detection, where we out-perform Aubry et al. for "chair" detection on a subset
of the Pascal VOC dataset.Comment: To appear in CVPR 201
A Survey on Object Recognition Using Deep Neural Networks
Deep Neural Networks as a means of objects detection and recognition is an active area of research and several discoveries have been made in this field. Here we will be discussing briefly about the history of research in the field of computer vision, mainly for the application of deep learning in object detection task and describe several of the recent advances in this ?eld. This paper describes a simple summary of the datasets and deep learning algorithms commonly used in computer vision, some of the applications of this ?eld have been provided
Object Detection based on Region Decomposition and Assembly
Region-based object detection infers object regions for one or more
categories in an image. Due to the recent advances in deep learning and region
proposal methods, object detectors based on convolutional neural networks
(CNNs) have been flourishing and provided the promising detection results.
However, the detection accuracy is degraded often because of the low
discriminability of object CNN features caused by occlusions and inaccurate
region proposals. In this paper, we therefore propose a region decomposition
and assembly detector (R-DAD) for more accurate object detection.
In the proposed R-DAD, we first decompose an object region into multiple
small regions. To capture an entire appearance and part details of the object
jointly, we extract CNN features within the whole object region and decomposed
regions. We then learn the semantic relations between the object and its parts
by combining the multi-region features stage by stage with region assembly
blocks, and use the combined and high-level semantic features for the object
classification and localization. In addition, for more accurate region
proposals, we propose a multi-scale proposal layer that can generate object
proposals of various scales. We integrate the R-DAD into several feature
extractors, and prove the distinct performance improvement on PASCAL07/12 and
MSCOCO18 compared to the recent convolutional detectors.Comment: Accepted to 2019 AAAI Conference on Artificial Intelligence (AAAI
Deep Learning Methods for Visual Fault Diagnostics of Dental X-ray Systems
Dental X-ray systems go through rigorous quality assurance protocols following their production and assembly. The protocols include tests, which address the image quality and find certain errors or artifacts that may be present in the images. Detecting faults from the images require human effort, experience, and time.
Recent advances in deep learning have proven them to be successful in image classification, object detection, machine translation. The applications of deep learning can be extended to fault detection in X-ray systems. This thesis work consists of surveying, applying, and developing state-of-art deep learning approaches for detection of visual faults or artifacts in the dental X-ray systems.
In this thesis, we have shown that deep learning methods can detect geometry and collimator artifacts from X-ray images efficiently and rapidly. This thesis is a precursor for further development of deep learning methods to include detection of wide range of faults and artifacts in X-ray systems to ease quality assurance, calibration, and device maintenance
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