A deep Q-learning network based active object detection model with a novel training algorithm for service robots-Reference-Cited by-同舟云学术

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A deep Q-learning network based active object detection model with a novel training algorithm for service robots

Published:2022-09-24 Issue:11 Volume:23 Page:1673-1683
ISSN:2095-9184
Container-title:Frontiers of Information Technology & Electronic Engineering
language:en
Short-container-title:Front Inform Technol Electron Eng

Author:

Liu Shaopeng^ORCID,Tian Guohui^ORCID,Cui Yongcheng,Shao Xuyang

Publisher

Zhejiang University Press

Subject

Electrical and Electronic Engineering,Computer Networks and Communications,Hardware and Architecture,Signal Processing

Link

https://link.springer.com/content/pdf/10.1631/FITEE.2200109.pdf

Reference22 articles.

1. Ammirato P, Poirson P, Park E, et al., 2017. A dataset for developing and benchmarking active vision. Proc IEEE Int Conf on Robotics and Automation, p.1378–1385. https://doi.org/10.1109/ICRA.2017.7989164

2. Ammirato P, Berg AC, Košecká J, 2018. Active vision dataset benchmark. Proc IEEE/CVF Conf on Computer Vision and Pattern Recognition Workshops, p.2046–2049. https://doi.org/10.1109/CVPRW.2018.00277

3. Dos Reis DH, Welfer D, De Souza Leite Cuadros MA, et al., 2019. Mobile robot navigation using an object recognition software with RGBD images and the YOLO algorithm. Appl Artif Intell, 33(14):1290–1305. https://doi.org/10.1080/08839514.2019.1684778

4. Duan KW, Bai S, Xie LX, et al., 2019. CenterNet: keypoint triplets for object detection. Proc IEEE/CVF Int Conf on Computer Vision, p.6568–6577. https://doi.org/10.1109/ICCV.2019.00667

5. Han XN, Liu HP, Sun FC, et al., 2019. Active object detection with multistep action prediction using deep Q-network. IEEE Trans Ind Inform, 15(6):3723–3731. https://doi.org/10.1109/TII.2019.2890849

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