Tourist Mobility Patterns: Faster R-CNN Versus YOLOv7 for Places of Interest Detection
The mobility of tourists plays a significant role in shaping their travel experiences and the overall dynamics of a destination. In recent years, the proliferation of social media platforms has provided a rich source of visual data, allowing us to leverage the abundance of pictures shared by tourist...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10323078/ |
| _version_ | 1827650396306276352 |
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| author | Intissar Hilali Abdullah Alfazi Nouha Arfaoui Ridha Ejbali |
| author_facet | Intissar Hilali Abdullah Alfazi Nouha Arfaoui Ridha Ejbali |
| author_sort | Intissar Hilali |
| collection | DOAJ |
| description | The mobility of tourists plays a significant role in shaping their travel experiences and the overall dynamics of a destination. In recent years, the proliferation of social media platforms has provided a rich source of visual data, allowing us to leverage the abundance of pictures shared by tourists to extract meaningful information. Using computer vision techniques and deep learning algorithms, such as object detection, it becomes possible to extract useful information from tourist pictures. In this study, we look for the best way to detect objects from pictures shared by tourists during their journey in order to determine their locations. To achieve our goal we propose a new methodology composed by; database creation, database annotation, preprocessing, deep learning implementation and evaluation. We implemented two deep learning object detection methods: YOLOv7 and Faster R-CNN. A dataset has been created to provide examples of training and testing for neuronal networks. The training was performed on various basic models, in order to increase the efficiency of the training time and to compare the results. We evaluated the results using three parameters: precision, recall and mAP. The results indicate that YOLOv7 has the precision and performance, with over 90 % mAP, 92.1 % precision and 92.7 % recall. |
| first_indexed | 2024-03-09T20:15:39Z |
| format | Article |
| id | doaj.art-8a220fecae5e4e6b97f02d1fc9b06379 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-09T20:15:39Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-8a220fecae5e4e6b97f02d1fc9b063792023-11-24T00:01:34ZengIEEEIEEE Access2169-35362023-01-011113014413015410.1109/ACCESS.2023.333463310323078Tourist Mobility Patterns: Faster R-CNN Versus YOLOv7 for Places of Interest DetectionIntissar Hilali0Abdullah Alfazi1https://orcid.org/0009-0004-3776-0297Nouha Arfaoui2Ridha Ejbali3https://orcid.org/0000-0002-8148-1621Research Team in Intelligent Machines (RTIM), National Engineering School of Gabes, University of Gabes, Gabes, TunisiaDepartment of Computer Science, College of Science and Humanities in Al-Aflaj, Prince Sattam bin Abdulaziz University, Al-Kharj, Saudi ArabiaResearch Team in Intelligent Machines (RTIM), National Engineering School of Gabes, University of Gabes, Gabes, TunisiaResearch Team in Intelligent Machines (RTIM), National Engineering School of Gabes, University of Gabes, Gabes, TunisiaThe mobility of tourists plays a significant role in shaping their travel experiences and the overall dynamics of a destination. In recent years, the proliferation of social media platforms has provided a rich source of visual data, allowing us to leverage the abundance of pictures shared by tourists to extract meaningful information. Using computer vision techniques and deep learning algorithms, such as object detection, it becomes possible to extract useful information from tourist pictures. In this study, we look for the best way to detect objects from pictures shared by tourists during their journey in order to determine their locations. To achieve our goal we propose a new methodology composed by; database creation, database annotation, preprocessing, deep learning implementation and evaluation. We implemented two deep learning object detection methods: YOLOv7 and Faster R-CNN. A dataset has been created to provide examples of training and testing for neuronal networks. The training was performed on various basic models, in order to increase the efficiency of the training time and to compare the results. We evaluated the results using three parameters: precision, recall and mAP. The results indicate that YOLOv7 has the precision and performance, with over 90 % mAP, 92.1 % precision and 92.7 % recall.https://ieeexplore.ieee.org/document/10323078/Conventional neural networkdeep learningfaster R-CNNimage processingobject detectiontourist service |
| spellingShingle | Intissar Hilali Abdullah Alfazi Nouha Arfaoui Ridha Ejbali Tourist Mobility Patterns: Faster R-CNN Versus YOLOv7 for Places of Interest Detection IEEE Access Conventional neural network deep learning faster R-CNN image processing object detection tourist service |
| title | Tourist Mobility Patterns: Faster R-CNN Versus YOLOv7 for Places of Interest Detection |
| title_full | Tourist Mobility Patterns: Faster R-CNN Versus YOLOv7 for Places of Interest Detection |
| title_fullStr | Tourist Mobility Patterns: Faster R-CNN Versus YOLOv7 for Places of Interest Detection |
| title_full_unstemmed | Tourist Mobility Patterns: Faster R-CNN Versus YOLOv7 for Places of Interest Detection |
| title_short | Tourist Mobility Patterns: Faster R-CNN Versus YOLOv7 for Places of Interest Detection |
| title_sort | tourist mobility patterns faster r cnn versus yolov7 for places of interest detection |
| topic | Conventional neural network deep learning faster R-CNN image processing object detection tourist service |
| url | https://ieeexplore.ieee.org/document/10323078/ |
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