robmarkcole/fire-detection-from-images
A fire detection system using YOLOv5 and neural networks to place bounding boxes around fire in images.
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This repository demonstrates a fire detection neural network model that identifies and localizes fire in images. It uses YOLOv5 (PyTorch) for object detection, achieving 0.657 mAP@.5 on a custom dataset of 1155 annotated images. The model can provide bounding box coordinates for fire regions, enabling automated responses like sprinkler activation or drone deployment. Alternative approaches using ResNet50 for classification are also explored.
Frequently asked
- What is robmarkcole/fire-detection-from-images?
- A fire detection system using YOLOv5 and neural networks to place bounding boxes around fire in images.
- Is fire-detection-from-images open source?
- Yes — robmarkcole/fire-detection-from-images is open source, released under the MIT license.
- What language is fire-detection-from-images written in?
- robmarkcole/fire-detection-from-images is primarily written in Jupyter Notebook.
- How popular is fire-detection-from-images?
- robmarkcole/fire-detection-from-images has 456 stars on GitHub.
- Where can I find fire-detection-from-images?
- robmarkcole/fire-detection-from-images is on GitHub at https://github.com/robmarkcole/fire-detection-from-images.