<![CDATA[In the realm of computer vision, object detection holds immense importance across applications such as surveillance and autonomous vehicles. This study addresses the critical challenge of human detection under low-light conditions, essential for nocturnal surveillance and autonomous driving systems. Focusing on the evolution of YOLO models, particularly YOLO - NAS and YOLOv8, a research gap is identified concerning their performance in low-light scenarios. The research conducts a detailed analysis of YOLO - NAS and YOLOv8 effectiveness in human detection under reduced ambient illumination. Object detection, vital in computer vision, faces challenges in low-light scenarios. This study concentrates on human detection due to its significance in night-time surveillance and autonomous driving. Despite YOLO models' evolution, a research gap exists in comparing their performance in low-light conditions. The study aims to fill this gap, providing insights for enhancing human detection methodologies in challenging lighting environments.]]>
Copyrights © 2023
