Real-Time Object Detection in Low-Light Environments using YOLOv8: A Case Study with a Custom Dataset

Abstract

Object detection in low-light conditions presents significant challenges due to the reduced visibility and poor illumination, particularly in real-time applications. This paper proposes a novel approach using the YOLOv8 model for real-time object detection in night-time conditions. A custom dataset comprising various objects captured in low-light environments was utilized to train and evaluate the model. The results demonstrate superior performance in terms of speed and accuracy compared to previous models, particularly YOLOv3. We also include an analysis of the model's real-time performance using a custom video feed. Our findings show that YOLOv8 outperforms earlier YOLO versions in detecting objects accurately and quickly in low-light, real-time scenarios, making it a promising solution for night-time surveillance and other security-related applications.