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Recognition of landmarks and tourist destinations by designing an image processing web service based on lightweight convolutional neural networks
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Mohammad Hassan Vahidnia *   |
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Abstract: (138 Views) |
The development of tourism has been further enhanced by the growth of information technology and the provision of location-based services. One of the key infrastructures in location-based services for developing applications in recent years has been the recognition of tourist destinations and landmarks from images, enabling the delivery of relevant information to users. In this study, a processing web service is designed and tested to enable real-time recognition of landmarks from images. To achieve this, an image repository of famous landmarks in Tehran was prepared, and a conventional Convolutional Neural Network (CNN) was compared with a lightweight pre-trained CNN. The lightweight pre-trained CNN outperformed the conventional model, achieving an overall accuracy of 92% compared to 71%. Additionally, it showed superiority in other performance metrics and required significantly less training time—5 minutes versus 90 minutes. Following this, a web processing service was developed using TensorFlow and Flask and deployed on the Render cloud service provider. Time-based and scalability evaluations produced satisfactory results, showing a minimal latency increase of 0.039 in the presence of concurrent users. Furthermore, in 90% of tests, the server successfully responded. This research demonstrated that the proposed approach could serve as a suitable infrastructure for recognizing and retrieving information about tourist destinations in application development. |
Article number: 3 |
| Keywords: Image processing, deep learning, lightweight convolutional neural network, cultural heritage, tourism, web services |
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Full-Text [PDF 1310 kb]
(55 Downloads)
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Type of Study: Research |
Subject:
GIS
Received: 2025/02/2
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Vahidnia M H. Recognition of landmarks and tourist destinations by designing an image processing web service based on lightweight convolutional neural networks. JGST 2025; 15 (1) : 3 URL: http://jgst.issgeac.ir/article-1-1212-en.html
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