AI-based Drone Assisted Human Rescue in Disaster Environments: Challenges and Opportunities (2406.15875v2)

Abstract: In this survey we are focusing on utilizing drone-based systems for the detection of individuals, particularly by identifying human screams and other distress signals. This study has significant relevance in post-disaster scenarios, including events such as earthquakes, hurricanes, military conflicts, wildfires, and more. These drones are capable of hovering over disaster-stricken areas that may be challenging for rescue teams to access directly. Unmanned aerial vehicles (UAVs), commonly referred to as drones, are frequently deployed for search-and-rescue missions during disaster situations. Typically, drones capture aerial images to assess structural damage and identify the extent of the disaster. They also employ thermal imaging technology to detect body heat signatures, which can help locate individuals. In some cases, larger drones are used to deliver essential supplies to people stranded in isolated disaster-stricken areas. In our discussions, we delve into the unique challenges associated with locating humans through aerial acoustics. The auditory system must distinguish between human cries and sounds that occur naturally, such as animal calls and wind. Additionally, it should be capable of recognizing distinct patterns related to signals like shouting, clapping, or other ways in which people attempt to signal rescue teams. To tackle this challenge, one solution involves harnessing AI to analyze sound frequencies and identify common audio signatures. Deep learning-based networks, such as convolutional neural networks (CNNs), can be trained using these signatures to filter out noise generated by drone motors and other environmental factors. Furthermore, employing signal processing techniques like the direction of arrival (DOA) based on microphone array signals can enhance the precision of tracking the source of human noises.

• The paper surveys the use of AI-powered drones for human rescue in disaster zones, focusing on acoustic detection challenges and the role of AI and signal processing.
• The paper details how AI models like CNNs and signal processing techniques are crucial for acoustic detection amidst noise and reviews applications in SAR, monitoring, and medical delivery.
• The paper highlights significant challenges such as drone synchronization, payload limitations, and network security, arguing for continued research to improve reliability and real-time analysis.

AI-based Drone Assisted Human Rescue in Disaster Environments: Challenges and Opportunities

The paper "AI-based Drone Assisted Human Rescue in Disaster Environments: Challenges and Opportunities" addresses the use of drones for human rescue operations in disaster-affected regions. Considering diverse calamities such as earthquakes, hurricanes, and wildfires, the paper provides an extensive survey on employing drones for detecting humans, primarily focusing on auditory cues like human screams and other distress signals.

In disaster scenarios, unmanned aerial vehicles (UAVs) have proven to be instrumental in search-and-rescue missions. These drones facilitate faster area coverage compared to ground-based efforts, as they use aerial imagery and thermal imaging to assess structural damage and detect human presence based on heat signatures. In some instances, drones are also used to deliver vital supplies to isolated zones.

One significant contribution of the paper is the in-depth discussion about challenges encountered in using aerial acoustics for human localization. Distinguishing human cries from ambient environmental sounds, such as animal calls or wind noise, requires sophisticated auditory systems. The integration of AI, particularly deep learning models like convolutional neural networks (CNNs), is crucial for analyzing sound frequencies and identifying human distress signals amidst noise interference from drone motors and the environment. Furthermore, the employment of signal processing techniques, such as direction of arrival (DOA), enhances the precision in pinpointing the source of human noises.

The paper introduces several applications of drone-assisted disaster management, including environmental monitoring, early warning systems, and information fusion and sharing. A focus on situational awareness highlights the importance of utilizing drones for maintaining up-to-date insights into evolving conditions in disaster areas, which aids in preparing and executing evacuation strategies efficiently.

UAV-assisted search and rescue (SAR) missions are a critical focal point, as drones enable rapid identification, medical care, and evacuation of individuals in distress. The paper points out that drones significantly reduce the time and risk associated with rescue operations, positioning them as essential assets in disaster management.

Regarding medical material delivery, drones can bridge the gap by providing quick access to necessary supplies, including medicine and care equipment, especially in areas cut off by disasters. This aspect of UAV utility extends into augmenting telemedicine platforms as well.

The paper also reviews state-of-the-art technologies like accelerometers, video, and thermal cameras, microphone arrays, radar, and infrared thermography, which drones use to enhance their detection, monitoring, and rescue capabilities.

A deep dive into the hardware employed for such operations reveals both the advantages and challenges of implementing UAV technology. While drones promise quick deployment and real-time data acquisition, they face issues like synchronization among multiple drones, limited payload capacities, and network security vulnerabilities. The paper discusses potential failure modes and design challenges such as UAV synchronization and network security.

In conclusion, the paper outlines a promising future direction for UAV technology in disaster response. By improving real-time predictive analysis, and refining sound analysis in noisy environments, drones could drastically change how human rescue operations are conducted. The paper argues for continued research and development to address existing challenges, which would maximize the impact and reliability of drones in saving lives during crises.