IoT‐Based Object Detection in Green Cities by Making Use of Data Center Based on YOLOv3 Model

The study provides a comprehensive data center monitoring and surveillance solution using Internet of Things (IoT)–based object identification in green cities based on the YOLOv3 (You Only Look Once, version 3) model. The proposed technique improves data center security by accurately recognizing and identifying many items of interest. Real-time object detection using computer vision and deep learning allows proactive monitoring and quick response to threats. The research uses the YOLOv3 model, a cutting-edge object detection technology known for its accuracy. Because it was trained on a massive collection of photographs suitable for data centers in green cities, the model can learn and recognize monitoring key features. Using annotated data during training, the model is customized to recognize IoT-based things of interest. To evaluate the proposed technique, several data center scenarios are tested. The research evaluates the model's accuracy, detection speed, and environmental robustness. The results reveal that the model can locate and identify data center servers, routers, cooling systems, and unauthorized personnel. The project also examines using thermal imaging and motion detection to increase system monitoring. These technologies allow the system to detect abnormalities and security breaches in real time, allowing early notifications and risk mitigation. The study considers IoT-based object detecting system processing efficiency. The research reveals that graphical processing unit parallel computing and model optimization may achieve real-time performance in massive data centers. This work provides a comprehensive data center monitoring and surveillance solution using IoT-based object identification in green cities based on the YOLOv3 model. Data centers need preventive security measures, and the research reveals how effectively the recommended technique can accurately identify and localize IoT-based items of interest. The results help develop data center monitoring systems that boost security, efficiency, and reliability. This paper provides a complete data center monitoring system utilizing YOLOv3 object detection algorithms. The proposed technique improves data center security by accurately recognizing and identifying many items of interest. Real-time object detection using computer vision and deep learning allows proactive monitoring and quick response to threats. The research used the cutting-edge YOLOv3 model, known for its accuracy. The algorithm has been trained on a vast data center– specific dataset to learn and recognize monitoring mission-critical items. Through extensive trials, the research evaluates the model's accuracy, detection speed, and resilience to data center ambient factors.