Tourist Mobility Patterns: Faster R-CNN Versus YOLOv7 for Places of Interest Detection-Reference-Cited by-同舟云学术

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Tourist Mobility Patterns: Faster R-CNN Versus YOLOv7 for Places of Interest Detection

Published:2023 Issue: Volume:11 Page:130144-130154
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Hilali Intissar¹,Alfazi Abdullah²^ORCID,Arfaoui Nouha¹,Ejbali Ridha¹^ORCID

Affiliation:

1. Research Team in Intelligent Machines (RTIM), National Engineering School of Gabes, University of Gabes, Gabes, Tunisia

2. Department of Computer Science, College of Science and Humanities in Al-Aflaj, Prince Sattam bin Abdulaziz University, Al-Kharj, Saudi Arabia

Funder

Prince Sattam bin Abdulaziz University

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/10005208/10323078.pdf?arnumber=10323078

Reference29 articles.

1. Jellytoring: Real-Time Jellyfish Monitoring Based on Deep Learning Object Detection

2. Tourism Object Detection Around Monumen Nasional (Monas) Using YOLO and RetinaNet

3. Tourist Attractions Classification using ResNet

4. A Computer Vision Framework for Automatic Description of Indian Monuments

5. Application of the Faster R-CNN Algorithm in Scene Recognition Function Design

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