Summary of Segnet: a Segmented Deep Learning Based Convolutional Neural Network Approach For Drones Wildfire Detection, by Aditya V. Jonnalagadda and Hashim A. Hashim
SegNet: A Segmented Deep Learning based Convolutional Neural Network Approach for Drones Wildfire Detection
by Aditya V. Jonnalagadda, Hashim A. Hashim
First submitted to arxiv on: 29 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Image and Video Processing (eess.IV)
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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 This research aims to enhance processing times and detection capabilities in Unmanned Aerial Vehicle (UAV) imagery for global wildfire detection, despite limited datasets. The proposed Segmented Neural Network (SegNet) selection approach reduces feature maps to boost time resolution and accuracy, significantly advancing processing speeds and accuracy in real-time wildfire detection. This paper contributes to increased processing speeds enabling real-time detection capabilities for wildfire, increased detection accuracy of wildfire, and improved detection capabilities of early wildfire. Employing Convolutional Neural Networks (CNNs) for image classification, the study emphasizes reducing irrelevant features vital for deep learning processes, especially in live feed data for fire detection. The proposed algorithm combats feature overload through segmentation, addressing challenges arising from diverse features like objects, colors, and textures. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research is trying to make it faster and more accurate to detect wildfires using drones. They’re proposing a new way of processing images that can help identify fires sooner and with better accuracy. The approach involves reducing the number of features in an image to focus on what’s really important, like objects, colors, and textures. This could help firefighters respond more quickly and effectively to wildfires. |
Keywords
» Artificial intelligence » Deep learning » Image classification » Neural network
