YES: You should Examine Suspect cues for low-light object detection

Abstract

Object detection in low-light conditions presents substantial challenges, particularly the issue we define as “low-light object-background cheating”. This phenomenon arises from uneven lighting, leading to blurred and inaccurate object edges. Most existing methods focus on basic feature enhancement and addressing the gap between normal-light and synthetic low-light conditions. However, they often overlook the complexities introduced by uneven lighting in real-world environments. To address this, we propose a novel low-light object detection framework, You Examine Suspect (YES), comprising two key components: the Optical Balance Enhancer (OBE) and the Entanglement Attenuation Module (EAM). The OBE emphasizes “balance” by employing techniques such as inverse tone mapping, white balance, and gamma correction to recover details in dark regions while adjusting brightness and contrast without introducing noise. The EAM focuses on “disentanglement” by analyzing both object regions and surrounding areas affected by lighting variations and integrating multi-scale contextual information to clarify ambiguous features. Extensive experiments on ExDark and Dark Face datasets demonstrate the superior performance of proposed YES, validating its effectiveness in low-light object detection tasks. The code will be available at https://github.com/Regina971/YES.