부산대학교 도서관

Advanced object detection (OD) 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 multidomain data, but performance remains poor because these methods train images from both low- and sufficient-luminance domains without a specific training policy. In this work, we present a lightweight framework for multidomain OD 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 OD and feature transformation in various domains. The proposed method achieves the state-of-the-art performance with a 9.95 improvement in average precision without incurring additional computational costs.