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SwinVid:Enhancing video object detection Using Swin Transformer

Computer Systems Science & Engineering 2024年第2期48卷 305-320页

作者： Abdelrahman Maharek Amr Abozeid Rasha Orban Kamal ElDahshan Computer Science Department Faculty of Artificial Intelligence and InformaticsBenhaEgypt Mathematics Department Faculty of SciencesAl-Azhar UniversityCairoEgypt Department of Computer Science College of Science and Arts in QurayyatJouf UniversitySakakaSaudi Arabia

What causes object detection in video to be less accurate than it is in still images?Because some video frames have degraded in appearance from fast movement,out-of-focus camera shots,and changes in *** reasons have made video object detection(VID)a growing area of research in recent *** object detection can be used for various healthcare applications,such as detecting and tracking tumors in medical imaging,monitoring the movement of patients in hospitals and long-term care facilities,and analyzing videos of surgeries to improve technique and ***,it can be used in telemedicine to help diagnose and monitor patients *** VID techniques are based on recurrent neural networks or optical flow for feature aggregation to produce reliable features which can be used for *** of those methods aggregate features on the full-sequence level or from nearby *** create feature maps,existing VID techniques frequently use Convolutional Neural Networks(CNNs)as the backbone *** the other hand,Vision Transformers have outperformed CNNs in various vision tasks,including object detection in still images and image *** propose in this research to use Swin-Transformer,a state-of-the-art Vision Transformer,as an alternative to CNN-based backbone networks for object detection in *** proposed architecture enhances the accuracy of existing VID *** ImageNet VID and EPIC KITCHENS datasets are used to evaluate the suggested *** have demonstrated that our proposed method is efficient by achieving 84.3%mean average precision(mAP)on ImageNet VID using less memory in comparison to other leading VID *** source code is available on the website https://***/amaharek/SwinVid.

关键词： video object detection vision transformers convolutional neural networks deep learning

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A novel robotic visual perception framework for underwater operation

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Frontiers of Information Technology & Electronic Engineering 2022年第11期23卷 1602-1619页

作者： Yue LU Xingyu CHEN Zhengxing WU Junzhi YU Li WEN State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China Ytech Kuaishou TechnologyBeijing 100085China State Key Laboratory for Turbulence and Complex Systems Department of Advanced Manufacturing and RoboticsCollege of EngineeringPeking UniversityBeijing 100871China School of Mechanical Engineering and Automation Beihang UniversityBeijing 100191China

Underwater robotic operation usually requires visual perception(e.g.,object detection and tracking),but underwater scenes have poor visual quality and represent a special domain which can affect the accuracy of visual *** addition,detection continuity and stability are important for robotic perception,but the commonly used static accuracy based evaluation(i.e.,average precision)is insufficient to reflect detector performance across *** response to these two problems,we present a design for a novel robotic visual perception ***,we generally investigate the relationship between a quality-diverse data domain and visual restoration in detection *** a result,although domain quality has an ignorable effect on within-domain detection accuracy,visual restoration is beneficial to detection in real sea scenarios by reducing the domain ***,non-reference assessments are proposed for detection continuity and stability based on object ***,online tracklet refinement is developed to improve the temporal performance of ***,combined with visual restoration,an accurate and stable underwater robotic visual perception framework is ***-overlap suppression is proposed to extend video object detection(VID)methods to a single-object tracking task,leading to the flexibility to switch between detection and *** experiments were conducted on the ImageNet VID dataset and real-world robotic tasks to verify the correctness of our analysis and the superiority of our proposed *** codes are available at https://***/yrqs/VisPerception.

关键词： Underwater operation Robotic perception Visual restoration video object detection

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