RodNet: An Advanced Multi-Domain Object Detection Approach using Feature Transformation with Generative Adversarial Networks

Da Wei Jaw, Shih Chia Huang, I. Chuan Lin, Cheng Zhang, Ching Chun Huang, Sy Yen Kuo

Research output: Contribution to journalArticlepeer-review

Abstract

Advanced object detection techniques have been widely studied in recent years and have been successfully applied in real-world applications. However, existing algorithms may struggle with nighttime image detection, especially in low-luminance conditions. Researchers have attempted to overcome this issue by collecting large amounts of multi-domain data, but performance remains poor because these methods train images from both low-luminance and sufficient-luminance domains without a specific training policy. In this work, we present a lightweight framework for multi-domain object detection using feature domain transformation with generative adversarial networks (GANs). The proposed GAN framework trains a generator network to transform features from the low-luminance domain to a sufficient-luminance domain, making the discriminator networks unable to distinguish whether the features were generated from a low-luminance or a normal image, and thus achieving luminance-invariant feature extraction. To preserve semantic meaning in the transformed features, a training policy has been introduced for object detection and feature transformation in various domains. The proposed method achieves state-of-the-art performance with a 9.95 improvement in average precision without incurring additional computational cost.

Original languageEnglish
Pages (from-to)1
Number of pages1
JournalIEEE Sensors Journal
DOIs
StateAccepted/In press - 2023

Keywords

  • Deep Learning
  • Detectors
  • Feature extraction
  • Generative adversarial networks
  • Generative Adversarial Networks
  • Generators
  • Low-Luminance
  • Object Detection
  • Object detection
  • Task analysis
  • Training

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