Benchmarking YOLOv5 models for improved human detection in search and rescue missions-Reference-Cited by-同舟云学术

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Benchmarking YOLOv5 models for improved human detection in search and rescue missions

Published:2024-03 Issue:1 Volume:22 Page:100243
ISSN:1674-862X
Container-title:Journal of Electronic Science and Technology
language:en
Short-container-title:Journal of Electronic Science and Technology

Author:

Bachir Namat,Memon Qurban Ali^ORCID

Publisher

Elsevier BV

Reference33 articles.

1. Lost Person Behavior: A Search and Rescue;Koester,2008

2. Convolutional neural networks for near real-time object detection from UAV imagery in avalanche search and rescue operations;Bejiga,2016

3. Search and rescue operation using UAVs: A case study;Martinez-Alpiste;Expert Syst. Appl.,2021

4. Opportunities and limitations of object based image analysis for detecting urban impervious and vegetated surfaces using true-colour aerial photography;Kampouraki,2008

5. Planning search and rescue missions for UAV teams;Baker,2016

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1. Explainable AI based automated segmentation and multi-stage classification of gastroesophageal reflux using machine learning techniques;Biomedical Physics & Engineering Express;2024-06-28

2. Case-Based Reasoning and Computer Vision for Cybersecurity;Advances in Information Security, Privacy, and Ethics;2024-02-23

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