| --- |
| license: mit |
| library_name: ultralytics |
| pipeline_tag: object-detection |
| tags: |
| - object-detection |
| - fire-hydrant |
| - fire-hydrants |
| - yolo |
| - yolo26 |
| - gis |
| - geospatial |
| - computer-vision |
| - street-view |
| - infrastructure |
| --- |
| |
| # 🚒Fire Hydrant Detection |
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| ### An open-source YOLO object detection model for automatically detecting fire hydrants in street-level imagery. |
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| [](https://huggingface.co/) |
| [](https://github.com/ultralytics/ultralytics) |
| [](#) |
| [](#) |
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| ## 🚒 Overview |
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| This project provides a YOLO-based computer vision model designed to detect **fire hydrants in street-level imagery**. |
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| The goal is to make it easier for GIS professionals, municipalities, utilities, researchers, and developers to automatically identify fire hydrants from imagery and support infrastructure inventory and mapping workflows. |
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| The model was trained specifically for the visual characteristics of fire hydrants appearing in street-level imagery. |
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| ### What can it do? |
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| Given an image such as: |
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| > 📷 Street-level imagery |
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| the model attempts to identify: |
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| > 🚒 **Fire Hydrant** |
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| and return its location using a bounding box and confidence score. |
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| --- |
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| ## 🎯 Model |
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| | Property | Value | |
| |---|---| |
| | Architecture | YOLO26n | |
| | Task | Object Detection | |
| | Classes | 1 | |
| | Detected class | Fire Hydrant | |
| | Image size | 640 × 640 | |
| | Annotation format | YOLO | |
| | Framework | Ultralytics | |
| | Model file | `best.pt` | |
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| --- |
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| ## 📊 Performance |
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| ## 📸 Example Results |
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| ### Fire Hydrant Detection |
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| # 🚀 Quick Start |
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| ## 1. Install Ultralytics |
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| ```bash |
| pip install ultralytics |
| ``` |
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| ## 2. Download the model |
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| Download **best.pt** from this repository |
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| ## 3. Run inference |
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| ```bash |
| from ultralytics import YOLO |
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| # Load the fire hydrant detection model |
| model = YOLO("best.pt") |
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| # Run detection |
| results = model("street_view.jpg") |
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| # Display the result |
| results[0].show() |
| |
| ``` |
| ## 4. Python Example |
| You can adjust the confidence threshold depending on your application. |
| Ex : **conf=0.25** will generally produce more detections, while **conf=0.75** will require greater confidence before returning a detection. |
| ```bash |
| from ultralytics import YOLO |
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| model = YOLO("best.pt") |
| |
| results = model.predict( |
| source="street_view.jpg", |
| conf=0.50, |
| imgsz=640 |
| ) |
| |
| for result in results: |
| result.show() |
| ``` |
| # 🗂️ Dataset |
| The model was trained using a dataset of street-level images containing fire hydrants. |
| Dataset characteristics |
| Images: 1,123 |
| Object class: Fire Hydrant |
| Annotation format: YOLO bounding boxes |
| Image source: Street-level imagery |
| Viewing directions: North, South, East, and West |
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| The dataset was created specifically for fire hydrant detection and infrastructure mapping applications. |
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| **⚠️ The original imagery used for training may be subject to third-party licensing and usage restrictions and is therefore not necessarily redistributed with this model.** |
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| # 🏋️ Training |
| The model was trained using the Ultralytics YOLO framework. |
| ``` bash |
| yolo detect train \ |
| data=dataset.yaml \ |
| model=yolo26n.pt \ |
| epochs=100 \ |
| imgsz=640 |
| ``` |
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| # 🗺️ GIS & Utility Applications |
| This model can potentially support a variety of GIS and infrastructure workflows. (Current famous models like struggle to detect fire hydrants or simply do not have a class for it) |
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| ## 1. Municipal GIS |
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| Automatically identify hydrants from street-level imagery and assist with updating infrastructure inventories. |
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| ## 2. Utility Mapping |
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| Use computer vision to help locate hydrants that may be missing from existing GIS datasets. |
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| ## 3. Asset Inventory |
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| Compare detected hydrants against existing GIS features to identify: |
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| Potential missing hydrants |
| Potential duplicate features |
| Possible positional discrepancies |
| Areas requiring field verification |
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| ## 4. Field Verification |
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| Use model detections as a starting point for GIS technicians and field crews. |
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| # ⚠️ Limitations |
| This model should be considered an assistive computer vision tool, not a replacement for field verification or authoritative GIS data. |
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| Performance may vary depending on: |
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| Image quality |
| Lighting conditions |
| Weather |
| Camera angle |
| Distance from the hydrant |
| Hydrant design |
| Vegetation |
| Vehicles or other obstructions |
| Image resolution |
| Geographic location |
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| Hydrants that are heavily occluded or visually different from those represented in the training data may be missed. |
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| Always verify important infrastructure information against authoritative GIS or field data. |
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| # 🌎 Geographic Considerations |
| The training imagery represents a particular geographic and environmental context. |
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| Because fire hydrant designs, colors, installation practices, vegetation, road environments, and imagery characteristics vary between regions, performance should be evaluated before deploying the model in a new geographic area. |
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| # 🤝 Contributing |
| Contributions are welcome. |
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| Possible areas for contribution include: |
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| Additional training images |
| Improved annotations |
| New geographic regions |
| Model evaluation |
| False-positive analysis |
| False-negative analysis |
| GIS integration |
| Detection workflows |
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| If you use this model in a project, feedback and performance results are appreciated |
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| # 📜 License |
| This model is released under the MIT License. |
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| Please review the licensing requirements of the underlying training imagery and any third-party components used with this project. |
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