UNLOCKING URBAN UAV POTENTIAL: ENHANCING CAPABILITIES WITH ARTIFICIAL NEURAL NETWORKS - A CASE STUDY FOR INSPECTION

Abstract

Unmanned Aerial Vehicles (UAVs), commonly known as drones, have revolutionized various industries with their ability to gather high-resolution data from aerial perspectives. In urban areas, UAVs offer immense potential for inspection and monitoring tasks, including infrastructure, buildings, and environmental assessments. However, their full potential can be further harnessed by leveraging Artificial Neural Networks (ANNs) to enhance data processing and analysis capabilities. This study presents a case study for urban areas inspection, where UAVs equipped with ANNs are utilized to improve the efficiency and accuracy of data interpretation. The research explores the integration of ANNs into UAV systems and demonstrates their impact on streamlining inspection processes. Through a combination of data collection, ANN training, and performance evaluation, the study highlights the advantages of this synergy in urban inspection applications. The findings showcase the potential of ANNs in expanding UAV capabilities, providing valuable insights for urban planners, infrastructure managers, and industries seeking to optimize inspection and monitoring tasks.

Keywords

Unmanned Aerial Vehicles (UAVs), Artificial Neural Networks (ANNs), urban areas inspection