Advancing low-light object detection with you only look once models: An empirical study and performance evaluation

Abstract

Low-light object detection is needed for ensuring security, enabling surveillance, and enhancing safety in diverse applications, including autonomous vehicles, surveillance systems, and search and rescue operations. A comprehensive study on low-light object detection is presented using state-of-the-art you only look once (YOLO) models, including YOLOv3, YOLOv5, YOLOv6, and YOLOv8, aiming to enhance detection performance under challenging low-light conditions. The ExDark dataset is a dataset that consists of adequate low-light images, modified to simulate realistic low-light scenarios, and employed for evaluation. The deep learning algorithm optimises YOLO's architecture for low-light detection by adapting the network structure and training strategies while preserving the algorithm's integrity. The experimental results show that YOLOv8 consistently outperforms baseline models, achieving significant improvements in accuracy and robustness in low-light scenarios. The deep learning algorithm that acquired the best score, YOLOv8s, had a mean average precision score of 0.5513. This work contributes to the field of low-light object detection, offering promising solutions for real-world applications like nighttime surveillance and autonomous navigation in low-light conditions, addressing the growing demand for advanced low-light object detection.