Cross-Domain Remote Sensing Image Object Detection Based on Multi-Scale Domain Adaptive Teacher Network

Hao Qi, Wenhui Chen, Xinyang Deng

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

Cross-domain remote sensing image object detection is highly dependent on the annotation of data and can be difficult to label. In addressing this problem, numerous researchers have proposed the utilization of pseudo-labeling approaches to mitigate domain shift, especially in two-stage detectors, which have shown significant effectiveness. However, the application of using a classical mean teacher network to generate pseudo-labels in single-stage detection models poses challenges. To address this problem, we propose a single-stage teacher model based on adversarial learning. Specifically, the teacher network is divided into classification and location sub-task branches, with an emphasis on optimizing pseudo-labeling. Compared to the mean teacher model, we pay more attention to the reliability of the boundary box. In the student network, this paper uses a multi-scale domain adaptive mechanism to extract domain invariant features of cross-domain images and uses an exponential sliding average to update the teacher network parameters, thereby improving pseudo labels and further narrowing the distribution differences. Experimental results confirm that compared to other single-stage detection methods for cross-domain object detection, this paper showcases a superior performance improvement in the field of remote sensing images.

Original languageEnglish
Title of host publicationProceedings of 2023 IEEE International Conference on Unmanned Systems, ICUS 2023
EditorsRong Song
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1026-1031
Number of pages6
ISBN (Electronic)9798350316308
DOIs
StatePublished - 2023
Event2023 IEEE International Conference on Unmanned Systems, ICUS 2023 - Hefei, China
Duration: 13 Oct 202315 Oct 2023

Publication series

NameProceedings of 2023 IEEE International Conference on Unmanned Systems, ICUS 2023

Conference

Conference2023 IEEE International Conference on Unmanned Systems, ICUS 2023
Country/TerritoryChina
CityHefei
Period13/10/2315/10/23

Keywords

  • adversarial learning
  • domain adaptation
  • object detection

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