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.
- Deep Learning
- Feature extraction
- Generative adversarial networks
- Generative Adversarial Networks
- Object Detection
- Object detection
- Task analysis