Drone Base System for Management of Electrical Infrastructure Damage in Disaster Areas

With the rising pace in technology taking place in the drone and artificial intelligence (AI) sectors, their amalgamation for the detection of infrastructure damage is a very feasible solution that is now available to cope with challenges in monitoring and maintaining critical infrastructure. This study describes a drone-based system for damage detection that works based on high-resolution imaging, thermal sensors, and metrics for structural integrity to identify and classify damage to infrastructure. The system is in a position to produce records that show temperature anomalies, values of structural integrity, and classifications of severity of damage after simulating data collection from drones. Severity of damage is dependent upon a rule-based analysis in which assessments on the basis of temperature anomalies as well as structural integrity are taken into consideration in declaring damage as “No Damage,” “Minor Damage,” “Moderate Damage,” “Severe Damage,” or “Critical Damage.” An AI model would further classify and predict damage severity on the basis of a calculated severity score through an efficient rule-based manner for automated damage detection. Visualization techniques such as bar graphs are used to display the results and provide a clear distribution of damages across the infrastructure. The results indicate that when used in combination with AI algorithms, such a system would increase efficiency in damage detection and accuracy of results, at a lower cost and less human error. Therefore, it provides a strong approach towards proactive maintenance of infrastructure and risk in which it ensures timely intervention without necessarily allowing a catastrophic failure to occur.