Abstract
Object recognition from video in low-light or night-time conditions poses significant challenges due to reduced visibility, noise, and infrared interference. Recent advancements in Artificial Intelligence (AI), especially in deep learning and computer vision, have enabled the development of robust models capable of detecting and classifying objects in night mode. This article reviews the core techniques and architectures used for object detection in dark environments, compares model performance using night datasets, and discusses hardware integration for real-time deployment. Key challenges such as dataset limitations, low signal-to-noise ratios, and real-time processing constraints are also addressed. The findings emphasize the growing capability of AI to enhance security, autonomous driving, and surveillance in low-light conditions.
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