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

• URL: http://arxiv.org/abs/2406.15875v2
• Date: Fri, 12 Jul 2024 20:34:34 GMT
• Title: AI-based Drone Assisted Human Rescue in Disaster Environments: Challenges and Opportunities
• Authors: Narek Papyan, Michel Kulhandjian, Hovannes Kulhandjian, Levon Hakob Aslanyan,
• Abstract summary: Unmanned aerial vehicles (UAVs) are frequently deployed for search-and-rescue missions during disaster situations. In some cases, larger drones are used to deliver essential supplies to people stranded in isolated disaster-stricken areas. The auditory system must distinguish between human cries and sounds that occur naturally, such as animal calls and wind. To tackle this challenge, one solution involves harnessing artificial intelligence (AI) to analyze sound frequencies and identify common audio signatures.
• Score: 10.803423394127458
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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 artificial intelligence (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.

Related papers

• Drone Acoustic Analysis for Predicting Psychoacoustic Annoyance via Artificial Neural Networks [0.0]
  This study builds upon prior research by examining the efficacy of various Deep Learning models in predicting Psychoacoustic Annoyance. The aim of this research is to improve our understanding of drone noise, aid in the development of noise reduction techniques, and encourage the acceptance of drone usage on public spaces.
  arXiv  Detail & Related papers  (2024-10-29T16:38:34Z)
• Sound-based drone fault classification using multitask learning [7.726132010393797]
  This paper proposes a sound-based deep neural network (DNN) fault classifier and drone sound dataset. The dataset was constructed by collecting the operating sounds of drones from microphones mounted on three different drones in an anechoic chamber. Using the acquired dataset, we train a classifier, 1DCNN-ResNet, that classifies the types of mechanical faults and their locations from short-time input waveforms.
  arXiv  Detail & Related papers  (2023-04-23T17:55:40Z)
• Unauthorized Drone Detection: Experiments and Prototypes [0.8294692832460543]
  We present a novel encryption-based drone detection scheme that uses a two-stage verification of the drone's received signal strength indicator ( RSSI) and the encryption key generated from the drone's position coordinates.
  arXiv  Detail & Related papers  (2022-12-02T20:43:29Z)
• The State of Aerial Surveillance: A Survey [62.198765910573556]
  This paper provides a comprehensive overview of human-centric aerial surveillance tasks from a computer vision and pattern recognition perspective. The main object of interest is humans, where single or multiple subjects are to be detected, identified, tracked, re-identified and have their behavior analyzed.
  arXiv  Detail & Related papers  (2022-01-09T20:13:27Z)
• A dataset for multi-sensor drone detection [67.75999072448555]
  The use of small and remotely controlled unmanned aerial vehicles (UAVs) has increased in recent years. Most studies on drone detection fail to specify the type of acquisition device, the drone type, the detection range, or the dataset. We contribute with an annotated multi-sensor database for drone detection that includes infrared and visible videos and audio files.
  arXiv  Detail & Related papers  (2021-11-02T20:52:03Z)
• A Multi-UAV System for Exploration and Target Finding in Cluttered and GPS-Denied Environments [68.31522961125589]
  We propose a framework for a team of UAVs to cooperatively explore and find a target in complex GPS-denied environments with obstacles. The team of UAVs autonomously navigates, explores, detects, and finds the target in a cluttered environment with a known map. Results indicate that the proposed multi-UAV system has improvements in terms of time-cost, the proportion of search area surveyed, as well as successful rates for search and rescue missions.
  arXiv  Detail & Related papers  (2021-07-19T12:54:04Z)
• Dogfight: Detecting Drones from Drones Videos [58.158988162743825]
  This paper attempts to address the problem of drones detection from other flying drones variations. The erratic movement of the source and target drones, small size, arbitrary shape, large intensity, and occlusion make this problem quite challenging. To handle this, instead of using region-proposal based methods, we propose to use a two-stage segmentation-based approach.
  arXiv  Detail & Related papers  (2021-03-31T17:43:31Z)
• Artificial Intelligence for UAV-enabled Wireless Networks: A Survey [72.10851256475742]
  Unmanned aerial vehicles (UAVs) are considered as one of the promising technologies for the next-generation wireless communication networks. Artificial intelligence (AI) is growing rapidly nowadays and has been very successful. We provide a comprehensive overview of some potential applications of AI in UAV-based networks.
  arXiv  Detail & Related papers  (2020-09-24T07:11:31Z)
• Real-Time Drone Detection and Tracking With Visible, Thermal and Acoustic Sensors [66.4525391417921]
  A thermal infrared camera is shown to be a feasible solution to the drone detection task. The detector performance as a function of the sensor-to-target distance is also investigated. A novel video dataset containing 650 annotated infrared and visible videos of drones, birds, airplanes and helicopters is also presented.
  arXiv  Detail & Related papers  (2020-07-14T23:06:42Z)
• AutoSOS: Towards Multi-UAV Systems Supporting Maritime Search and Rescue with Lightweight AI and Edge Computing [27.15999421608932]
  This paper presents the research directions of the AutoSOS project, where we work in the development of an autonomous multi-robot search and rescue assistance platform. The platform is meant to perform reconnaissance missions for initial assessment of the environment using novel adaptive deep learning algorithms. When drones find potential objects, they will send their sensor data to the vessel to verity the findings with increased accuracy.
  arXiv  Detail & Related papers  (2020-05-07T12:22:15Z)
• Amateur Drones Detection: A machine learning approach utilizing the acoustic signals in the presence of strong interference [10.74088633638169]
  Unmonitored deployment of UAVs called the amateur drones (AmDr) can lead to serious security threats and risk to human life and infrastructure. In this paper, we propose an efficient machine learning approach to detect various acoustic signals i.e., sounds of bird, airplanes, thunderstorm, rain, wind and the UAVs.
  arXiv  Detail & Related papers  (2020-02-28T17:28:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.