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Akademický článek

Study on deformation measurement method based on structured light point clouds

Autor: XIAO Weizhong, YUAN Yingtao, GE Zhendong, GUO Xiang, SUO Tao, YU Qifeng

Publikováno v: Xibei Gongye Daxue Xuebao, Vol 41, Iss 1, Pp 47-55 (2023)

For the requirement of structural deformation measurement, a deformation measurement method based on structured light point clouds is proposed. A structured light measurement system is built, and the calibration process based on the assumption of the

Externí odkaz: https://doaj.org/article/2277c82003cd4d468c0469b9fef10bcc

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Line-based 6-DoF Object Pose Estimation and Tracking With an Event Camera

Autor: Liu, Zibin, Guan, Banglei, Shang, Yang, Yu, Qifeng, Kneip, Laurent

Pose estimation and tracking of objects is a fundamental application in 3D vision. Event cameras possess remarkable attributes such as high dynamic range, low latency, and resilience against motion blur, which enables them to address challenging high

Externí odkaz: http://arxiv.org/abs/2408.03225

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Small Aerial Target Detection for Airborne Infrared Detection Systems using LightGBM and Trajectory Constraints

Autor: Sun, Xiaoliang, Guo, Liangchao, Zhang, Wenlong, Wang, Zi, Yu, Qifeng

Publikováno v: IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 14 9959-9973 2021

Factors, such as rapid relative motion, clutter background, etc., make robust small aerial target detection for airborne infrared detection systems a challenge. Existing methods are facing difficulties when dealing with such cases. We consider that a

Externí odkaz: http://arxiv.org/abs/2407.01278

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Oriented Object Detection in Optical Remote Sensing Images using Deep Learning: A Survey

Autor: Wang, Kun, Wang, Zi, Li, Zhang, Su, Ang, Teng, Xichao, Liu, Minhao, Yu, Qifeng

Oriented object detection is one of the most fundamental and challenging tasks in remote sensing, aiming to locate and classify objects with arbitrary orientations. Recent years have witnessed remarkable progress in oriented object detection using de

Externí odkaz: http://arxiv.org/abs/2302.10473

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Bridging the Domain Gap in Satellite Pose Estimation: a Self-Training Approach based on Geometrical Constraints

Autor: Wang, Zi, Chen, Minglin, Guo, Yulan, Li, Zhang, Yu, Qifeng

Recently, unsupervised domain adaptation in satellite pose estimation has gained increasing attention, aiming at alleviating the annotation cost for training deep models. To this end, we propose a self-training framework based on the domain-agnostic

Externí odkaz: http://arxiv.org/abs/2212.12103

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