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author:

Huang, Feng (Huang, Feng.) [1] | Huang, Wei (Huang, Wei.) [2] | Wu, Xianyu (Wu, Xianyu.) [3] (Scholars:吴衔誉)

Indexed by:

EI Scopus SCIE

Abstract:

Due to the complexity of real optical flow capture, the existing research still has not performed real optical flow capture of infrared (IR) images with the production of an optical flow based on IR images, which makes the research and application of deep learning-based optical flow computation limited to the field of RGB images only. Therefore, in this paper, we propose a method to produce an optical flow dataset of IR images. We utilize the RGB-IR cross-modal image transformation network to rationally transform existing RGB image optical flow datasets. The RGB-IR cross-modal image transformation is based on the improved Pix2Pix implementation, and in the experiments, the network is validated and evaluated using the RGB-IR aligned bimodal dataset M3FD. Then, RGB-IR cross-modal transformation is performed on the existing RGB optical flow dataset KITTI, and the optical flow computation network is trained using the IR images generated by the transformation. Finally, the computational results of the optical flow computation network before and after training are analyzed based on the RGB-IR aligned bimodal data.

Keyword:

deep neural network infrared image optical flow

Community:

  • [ 1 ] [Huang, Feng]Fuzhou Univ, Sch Mech Engn & Automat, Fuzhou 350108, Peoples R China
  • [ 2 ] [Huang, Wei]Fuzhou Univ, Sch Mech Engn & Automat, Fuzhou 350108, Peoples R China
  • [ 3 ] [Wu, Xianyu]Fuzhou Univ, Sch Mech Engn & Automat, Fuzhou 350108, Peoples R China

Reprint 's Address:

  • 吴衔誉

    [Wu, Xianyu]Fuzhou Univ, Sch Mech Engn & Automat, Fuzhou 350108, Peoples R China

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Source :

SENSORS

ISSN: 1424-8220

Year: 2024

Issue: 5

Volume: 24

3 . 4 0 0

JCR@2023

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 12

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