Summary of Automatic Location Detection Based on Deep Learning, by Anjali Karangiya et al.
Automatic location detection based on deep learning
by Anjali Karangiya, Anirudh Sharma, Divax Shah, Kartavya Badgujar, Chintan Thacker, Dainik Dave
First submitted to arxiv on: 16 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed image classification system is a deep learning-based solution designed to identify and classify images of Indian cities. The model classifies images into five categories: Ahmedabad, Delhi, Kerala, Kolkata, and Mumbai, recognizing distinct features and characteristics of each city/state. A vanilla Convolutional Neural Network (CNN) achieved commendable accuracy, while leveraging the VGG16 model improved performance with a test accuracy of 63.6%. The study’s findings demonstrate potential applications in tourism, urban planning, and real-time location identification systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine looking at pictures of Indian cities like Ahmedabad or Mumbai, and wanting to know which city it is. A team of researchers has developed a system that can do just that! They used special computer algorithms called deep learning to teach their model to recognize pictures of different Indian cities. The model was tested on many images and was able to correctly identify the city in most cases. This could be useful for things like planning travel or helping with urban development. |
Keywords
* Artificial intelligence * Cnn * Deep learning * Image classification * Neural network
