An Efficient and Robust Night-Time Surveillance Object Detection System Using YOLOv8 and High-Performance Computing

Abstract

Surveillance is critical in ensuring security and safety in the modern world, with daytime surveillance systems operating under favorable conditions. In contrast, nighttime surveillance presents a far more complex challenge due to reduced visibility and varying lighting conditions. Performing object detection at night poses a distinct challenge due to limited visibility and low-light conditions. Most conventional object detection methodologies struggle to perform accurately for small and distant objects in low light conditions, leading to deep learning techniques like You Only Look Once (YOLO), which gained significant attention due to its ability to achieve higher detection accuracies at incredible speeds. Advancements in deep learning methods have led to substantial improvements, but object detection in low-light scenarios remains a formidable challenge. This article proposes a method based on YOLOv8, a computer vision and deep learning-based technology on the ExDark dataset, which is a collection of images in low-light conditions using the maximum resources of high-performance computing (HPC) for an enhanced object detection model. The study demonstrates the effectiveness of YOLOv8 in overcoming limitations seen in other object detection frameworks, including Fast RCNN, Faster R-CNN, and SSD, by utilizing a real-time processing approach that maintains high accuracy even under challenging conditions. This study explores various image augmentation techniques, optimization strategies, hyperparameters tuning, and optional techniques to improve the model's detection capabilities, further increasing object detection robustness in surveillance tasks. The results of this study showcase that the object detection effectiveness of YOLOv8 is promising and achieves a significant Precision of 0.908, Recall of 0.819, and accuracy mAP of 0.886, as well as other metrics in low light and nighttime surveillance for small and distant objects. This study contributes to the ongoing development of intelligent surveillance systems by comprehensively evaluating YOLOv8 every model's nano(n), small(s), medium(m), large (l), and extra-large (x) performance in low-light conditions. It offers insights into improving object detection in critical security operations.