YOLOv6 for Fire Images detection-Reference-Cited by-同舟云学术

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YOLOv6 for Fire Images detection

Published:2023-10-03 Issue: Volume: Page:
ISSN:
Container-title:2023 International Conference on Cyberworlds (CW)
language:
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Author:

Jabnouni Hedi¹,Arfaoui Imen²,Cherni Mohamed Ali²,Bouchouicha Moez³,Sayadi Mounir²

Affiliation:

1. Université de Sousse,Institut Supérieur d’Informatique et des Techniques de Communication de H. Sousse,H. Sousse,Tunisie,4011

2. Université de Tunis,LR13 ES03 SIME, ENSIT, Montfleury,Tunis,Tunisia,1008

3. Université de Toulon, Aix Marseille Univ,CNRS, LIS,Toulon,France,83041

Funder

Ministry of Foreign Affairs

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10336928/10337517/10337537.pdf?arnumber=10337537

Reference9 articles.

1. Early fire detection using convolutional neural networks during surveillance for effective disaster management

2. Image fire detection algorithms based on convolutional neural networks

3. Fire-Net: A Deep Learning Framework for Active Forest Fire Detection

4. Unmanned aerial vehicle (uav) based forest fire detection and monitoring for reducing false alarms in forest-fires;Sengan;Computer Communications,2019

5. Adversarial Fusion Network for Forest Fire Smoke Detection

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