Summary of Comprehensive and Comparative Analysis Between Transfer Learning and Custom Built Vgg and Cnn-svm Models For Wildfire Detection, by Aditya V. Jonnalagadda et al.
Comprehensive and Comparative Analysis between Transfer Learning and Custom Built VGG and CNN-SVM Models for Wildfire Detection
by Aditya V. Jonnalagadda, Hashim A. Hashim, Andrew Harris
First submitted to arxiv on: 12 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 A new paper examines the efficiency and effectiveness of transfer learning for wildfire detection using deep learning models. The researchers compare three purpose-built models with three pre-trained models, training and evaluating them on a dataset that captures complexities like lighting conditions and terrain. They assess performance metrics like accuracy, precision, recall, and F1 score to understand the advantages and disadvantages of transfer learning in this domain. By doing so, they contribute valuable insights to guide future AI and ML research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Wildfire prediction is crucial for saving lives and property. Scientists are using artificial intelligence (AI) and machine learning (ML) to improve detection. A new study compares different models to see which one works best. They used a special dataset that shows what wildfires look like from different angles, times of day, and terrain types. By testing the models and looking at how well they do, the researchers found out if using pre-trained models is better than creating models from scratch for this job. |
Keywords
» Artificial intelligence » Deep learning » F1 score » Machine learning » Precision » Recall » Transfer learning
