Analysis of real-time multi-surveillance detection model using YOLO v5

Abstract

Implementation of this advanced nighttime monitoring system provides one of the basic requirements toward the creation of an intelligent urban environment. The nighttime effective monitoring is highly enabled due to seamless integration of multi-directional cameras working as advanced sensors enhancing security measures in smart cities. This paper addresses the mentioned issues directly by proposing the you only look once version 5 (YOLOv5) model dedicated to object detection. It is experimentally confirmed, based on the dataset results, that the mean average precision of YOLOv5 multi-scale (YOLOv5MS) reaches an impressive 88.7%. The results unmistakably confirm domination of the model and its good ability to work over a network of more than 50 security cameras under the high restrictions of our operation. The use of state-of-art nighttime surveillance systems is an important constituent element in the construction of smart urban environment. The smooth interaction between multiple-angle cameras, which work as perceptive sensors, substantially upgrades the functionality of nighttime surveillance and strengthens security practices for smart cities. The current work presented the YOLOv5 model specifically designed for the task of target detection, targeting these issues head-on. The empirical data obtained from the dataset point to an outstanding mean average precision (mAP) of 88.7% for YOLOv5MS. Such results clearly prove the superiority of the model and demonstrate its excellent performance in a network of more than 50 security cameras under our harsh operational conditions.