2PCNet: Two-Phase Consistency Training for Day-to-Night Unsupervised Domain Adaptive Object Detection

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2PCNet: Two-Phase Consistency Training for Day-to-Night Unsupervised Domain Adaptive Object Detection
Title:
2PCNet: Two-Phase Consistency Training for Day-to-Night Unsupervised Domain Adaptive Object Detection
Journal Title:
The IEEE/CVF Conference on Computer Vision and Pattern Recognition 2023
DOI:
Publication Date:
18 June 2023
Citation:
NA
Abstract:
Object detection at night is a challenging problem due to the absence of night image annotations. Despite several domain adaptation methods, achieving high-precision results remains an issue. False-positive error propagation is still observed in methods using the well-established studentteacher framework, particularly for small-scale and lowlight objects. This paper proposes a two-phase consistency unsupervised domain adaptation network, 2PCNet, to address these issues. The network employs high-confidence bounding-box predictions from the teacher in the first phase and appends them to the student’s region proposals for the teacher to re-evaluate in the second phase, resulting in a combination of high and low confidence pseudo-labels. The night images and pseudo-labels are scaled-down before being used as input to the student, providing stronger smallscale pseudo-labels. To address errors that arise from lowlight regions and other night-related attributes in images, we propose a night-specific augmentation pipeline called NightAug. This pipeline involves applying random augmentations, such as glare, blur, and noise, to daytime images. Experiments on publicly available datasets demonstrate that our method achieves superior results to state-ofthe-art methods by 20%, and to supervised models trained directly on the target data.
License type:
Publisher Copyright
Funding Info:
This research is supported by core funding from: Institute for Infocomm Research
Grant Reference no. : SC20-RD150

This research / project is supported by the Ministry of Education (MOE) - Academic Research Fund Tier 2
Grant Reference no. : MOE2019-T2-1-130
Description:
© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
ISSN:
1063-6919
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