Multi-surveillance detection for night surveillance using modified yolo v5 algorithm based optical communication

Abstract Night vision significantly impacts our daily visual efficiency and has more impact in communications networks. This work primarily emphasises enhancing the safety and security of individuals through advancements in night vision systems. While research on night vision is crucial for addressing contemporary social challenges, there remains a scarcity of databases tailored for deep-learning investigations in this domain. To address these challenges, this study evaluates the performance of three distinct object detection models: Yolo v3 achieving a mean average precision (mAP) of 87.9 % at 45 frames per second (FPS), Yolo v5 with a mAP of 88.7 % at 20 FPS, and modified Yolo v5 with a remarkable mAP of 95.1 % at 79 FPS. From the results, it is proven that this algorithm successfully detects classes including humans, animals, and vehicles.