Multidomain Object Detection Framework Using Feature Domain Knowledge Distillation

Da Wei Jaw, Shih Chia Huang*, Zhi Hui Lu*, Benjamin C.M. Fung, Sy Yen Kuo

*Corresponding author for this work

Research output: Contribution to journalJournal Article peer-review

Abstract

Object detection techniques have been widely studied, utilized in various works, and have exhibited robust performance on images with sufficient luminance. However, these approaches typically struggle to extract valuable features from low-luminance images, which often exhibit blurriness and dim appearence, leading to detection failures. To overcome this issue, we introduce an innovative unsupervised feature domain knowledge distillation (KD) framework. The proposed framework enhances the generalization capability of neural networks across both low- and high-luminance domains without incurring additional computational costs during testing. This improvement is made possible through the integration of generative adversarial networks and our proposed unsupervised KD process. Furthermore, we introduce a region-based multiscale discriminator designed to discern feature domain discrepancies at the object level rather than from the global context. This bolsters the joint learning process of object detection and feature domain distillation tasks. Both qualitative and quantitative assessments shown that the proposed method, empowered by the region-based multiscale discriminator and the unsupervised feature domain distillation process, can effectively extract beneficial features from low-luminance images, outperforming other state-of-the-art approaches in both low- and sufficient-luminance domains.

Original languageEnglish
Pages (from-to)4643-4651
Number of pages9
JournalIEEE Transactions on Cybernetics
Volume54
Issue number8
DOIs
StatePublished - 08 2024
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2013 IEEE.

Keywords

  • Generative adversarial networks (GANs)
  • object detection
  • unsupervised knowledge distillation (KD)

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