A Novel Framework to Evaluate and Train Object Detection Models for Real-Time Victims Search and Rescue at Sea with Autonomous Unmanned Aerial Systems Using High-Fidelity Dynamic Marine Simulation Environment-Reference-Cited by-同舟云学术

A Novel Framework to Evaluate and Train Object Detection Models for Real-Time Victims Search and Rescue at Sea with Autonomous Unmanned Aerial Systems Using High-Fidelity Dynamic Marine Simulation Environment

Published:2023-01 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW)
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Author:

Poudel Rajeev¹,Lima Luciano¹,Andrade Fabio²

Affiliation:

1. University of South-Eastern Norway

2. University of South-Eastern Norway NORCE Norwegian Research Centre

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10029377/10029379/10031171.pdf?arnumber=10031171

Reference27 articles.

1. A New Simulation Environment Based on Airsim, ROS, and PX4 for Quadcopter Aircrafts

2. Simulating differential games with improved fidelity to better inform cooperative & adversarial two vehicle UAV flight

3. Drone-surveillance for search and rescue in natural disaster

4. A Novel Real-Time Unmanned Aerial Vehicles-based Disaster Management Framework

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1. Simulation Environment Implementation for Generation of Training Samples;2024 25th International Carpathian Control Conference (ICCC);2024-05-22

2. Empowering Search and Rescue Operations with Big Data Technology: A Comprehensive Study of YOLOv8 Transfer Learning for Transportation Safety;2023 IEEE International Conference on Big Data (BigData);2023-12-15

3. Development of a Deep-Learning Model with Maritime Environment Simulation for Detection of Distress Ships from Drone Images;KOREAN J REMOTE SENS;2023