Summary of Image Processing Based Forest Fire Detection, by Vipin V
Image Processing Based Forest Fire Detection
by Vipin V
First submitted to arxiv on: 7 Jun 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 A novel image processing technique is presented for detecting forest fires. A rule-based color model classifies fire pixels based on RGB and YCbCr color spaces. The use of YCbCr space allows for more effective separation of luminance from chrominance compared to RGB. The proposed algorithm’s performance is evaluated using standard methods on two image sets: one with fire, the other with fire-like regions. Results show higher detection rates and lower false alarm rates compared to standard methods. Due to its low computational cost, this method can be used for real-time forest fire detection. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Scientists have developed a new way to detect forest fires using images. They use special rules to identify pixels that might be on fire. This helps them separate the brightness and color of the image. The team tested their algorithm on two sets of pictures: one with actual fires, and another with areas that look like they could be on fire. Their method works better than others at finding fires without false alarms. Because it’s quick to calculate, this technique can be used to detect forest fires in real-time. |
