270 research outputs found
A Survey of Deep Learning-Based Object Detection
Object detection is one of the most important and challenging branches of
computer vision, which has been widely applied in peoples life, such as
monitoring security, autonomous driving and so on, with the purpose of locating
instances of semantic objects of a certain class. With the rapid development of
deep learning networks for detection tasks, the performance of object detectors
has been greatly improved. In order to understand the main development status
of object detection pipeline, thoroughly and deeply, in this survey, we first
analyze the methods of existing typical detection models and describe the
benchmark datasets. Afterwards and primarily, we provide a comprehensive
overview of a variety of object detection methods in a systematic manner,
covering the one-stage and two-stage detectors. Moreover, we list the
traditional and new applications. Some representative branches of object
detection are analyzed as well. Finally, we discuss the architecture of
exploiting these object detection methods to build an effective and efficient
system and point out a set of development trends to better follow the
state-of-the-art algorithms and further research.Comment: 30 pages,12 figure
Neural radiance fields in the industrial and robotics domain: applications, research opportunities and use cases
The proliferation of technologies, such as extended reality (XR), has
increased the demand for high-quality three-dimensional (3D) graphical
representations. Industrial 3D applications encompass computer-aided design
(CAD), finite element analysis (FEA), scanning, and robotics. However, current
methods employed for industrial 3D representations suffer from high
implementation costs and reliance on manual human input for accurate 3D
modeling. To address these challenges, neural radiance fields (NeRFs) have
emerged as a promising approach for learning 3D scene representations based on
provided training 2D images. Despite a growing interest in NeRFs, their
potential applications in various industrial subdomains are still unexplored.
In this paper, we deliver a comprehensive examination of NeRF industrial
applications while also providing direction for future research endeavors. We
also present a series of proof-of-concept experiments that demonstrate the
potential of NeRFs in the industrial domain. These experiments include
NeRF-based video compression techniques and using NeRFs for 3D motion
estimation in the context of collision avoidance. In the video compression
experiment, our results show compression savings up to 48\% and 74\% for
resolutions of 1920x1080 and 300x168, respectively. The motion estimation
experiment used a 3D animation of a robotic arm to train Dynamic-NeRF (D-NeRF)
and achieved an average peak signal-to-noise ratio (PSNR) of disparity map with
the value of 23 dB and an structural similarity index measure (SSIM) 0.97
Simple Baseline for Vehicle Pose Estimation: Experimental Validation
Significant progress on human and vehicle pose estimation has been achieved in recent years. The performance of these methods has evolved from poor to remarkable in just a couple of years. This improvement has been obtained from increasingly complex architectures. In this paper, we explore the applicability of simple baseline methods by adding a few deconvolutional layers on a backbone network to estimate heat maps that correspond to the vehicle keypoints. This approach has been proven to be very effective for human pose estimation. The results are analyzed on the PASCAL3DC dataset, achieving state-of-the-art results. In addition, a set of experiments has been conducted to study current shortcomings in vehicle keypoints labelling, which adversely affect performance. A new strategy for de ning vehicle keypoints is presented and validated with our customized dataset with extended keypoints
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