Drone Captured Image Analysis of Faulty Antennas in 5G Networks Using AI & ML During Natural Disasters

Natural disasters pose a significant threat to telecommunications infrastructure, with antennas installed on buildings being particularly vulnerable to collapse. The ability to identify damaged antennas and other hardware quickly is crucial in maintaining network uptime and ensuring the safety of repair crews. This study uses drones and techniques to capture 5G Antenna images and analyze them for faulty antennas during natural disasters. The approach involves using image processing algorithms to detect and classify damaged antennas, providing detailed information for repair crews to act quickly and efficiently. The study also explores the use of AI-powered predictive maintenance to prevent faults from occurring and improve the overall reliability of the network. The results show that the proposed approach can significantly enhance the resilience of 5G networks during natural disasters, providing a more reliable and efficient telecom infrastructure. The use of drones and AI/ML especially YOLOv4 -tiny techniques in this context represents an exciting opportunity for growth and innovation in the telecommunications industry.