The Duo of Visual Servoing and Deep Learning-Based Methods for Situation-Aware Disaster Management: A Comprehensive Review

Abstract

Unmanned aerial vehicles (UAVs) have become essential in disaster management due to their ability to provide real-time situational awareness and support decision-making processes. Visual servoing, a technique that uses visual feedback to control the motion of a robotic system, has been used to improve the precision and accuracy of UAVs in disaster scenarios. The study integrates visual servoing to enhance UAV precision while exploring recent advancements in deep learning. This integration enhances the precision and efficiency of disaster response by enabling UAVs to navigate complex environments, identify critical areas for intervention, and provide actionable insights to decision-makers in real time. It discusses disaster management aspects like search and rescue, damage assessment, and situational awareness, while also analyzing the challenges associated with integrating visual servoing and deep learning into UAVs. This review article provides a comprehensive analysis to offer real-time situational awareness and decision support in disaster management. It highlights that deep learning along with visual servoing enhances precision and accuracy in disaster scenarios. The analysis also summarizes the challenges and the need for high computational power, data processing, and communication capabilities. UAVs, especially when combined with visual servoing and deep learning, play a crucial role in disaster management. The review underscores the potential benefits and challenges of integrating these technologies, emphasizing their significance in improving disaster response and recovery, with possible means of enhanced situational awareness and decision-making.