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--- |
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license: agpl-3.0 |
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pipeline_tag: depth-estimation |
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tags: |
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- ONNX |
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- Indoor |
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- Small |
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- Large |
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- Base |
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- DepthAnything |
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- Depth |
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- UniDepth |
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- YOLO |
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base_model: |
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- Ultralytics/YOLO11 |
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- lpiccinelli/unidepth-v2-vits14 |
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- lpiccinelli/unidepth-v2-vitb14 |
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- lpiccinelli/unidepth-v2-vitl14 |
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- depth-anything/Depth-Anything-V2-Metric-Indoor-Small-hf |
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- depth-anything/Depth-Anything-V2-Metric-Indoor-Base-hf |
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- depth-anything/Depth-Anything-V2-Metric-Indoor-Large-hf |
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--- |
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<p align="center"> |
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<img src="assets/EdgeCV4Safety_logo.png" alt="EdgeCV4Safety Logo" width="18%"/> |
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</p> |
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# EdgeCV4Safety: AI-Driven Contextual Safety System for Industry 4.0/5.0 |
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**EdgeCV4Safety-Models** contains **ONNX** **_depth estimation_** and **_object detection_** models. Specifically **UniDepth v2** and **Depth Anything v2** for depth estimation and **YOLO11** for object detecion. |
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This repo was created to support the main GH repository [**Edge4CVSafety**](https://github.com/EdgeCV4Safety/EdgeCV4Safety.git). This project implements a **modular and scalable Computer Vision (CV) system** designed to replace traditional physical barriers, enhancing **worker safety** in industrial settings (Industry 4.0/5.0). The core objective is to achieve **contextual control** of machinery based on the dynamic state of the surrounding work environment. |
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The system continuously monitors a defined workspace. Upon the detection of personnel entering this area, appropriate countermeasures are instantly triggered, influencing machinery behavior to prevent hazardous situations. This compartmentalized architecture promotes high **modularity and scalability**. |
