Universe

DPI_detection Computer Vision Dataset

byNadiaTask:
Object Detection
License:CC BY 4.0132 views13 downloads
See all 1.6k imagesDPI_detection DPI sample showing Caschetto, No Caschetto, No GiletDPI_detection DPI sample showing No Caschetto, No Cuffie, No Gilet, No OcchialiDPI_detection DPI sample showing Caschetto, No Cuffie, No Gilet, No OcchialiDPI_detection DPI sample showing Caschetto, GiletDPI_detection DPI sample showing Cuffie, Gilet, No CaschettoDPI_detection DPI sample showing Caschetto, No Gilet, No OcchialiDPI_detection DPI sample showing Cuffie, No Caschetto, No Gilet, No OcchialiDPI_detection DPI sample showing Caschetto, Cuffie, No Gilet, OcchialiDPI_detection DPI sample showing Caschetto, Gilet, No Cuffie, No OcchialiDPI_detection DPI sample showing No Caschetto, No Cuffie, No Gilet, OcchialiDPI_detection DPI sample showing Caschetto, Cuffie, No Gilet, No OcchialiDPI_detection DPI sample showing Gilet, No Caschetto, No Cuffie, No Occhiali

About DPI_detection Dataset

Here are a few use cases for this project:

  1. Workplace Safety Compliance Monitoring: The DPI_detection model can be integrated into video surveillance systems at workplaces such as construction sites, factories, or laboratories to monitor employees and ensure they are wearing appropriate personal protective equipment (PPE) like helmets, safety vests, ear protection, and goggles. This would help prevent accidents and injuries, ensuring a safer work environment.

  2. Smart Access Control Systems: This computer vision model can be used to build access control systems at sites where the correct PPE is mandatory. The model can identify if an individual is wearing the required PPE before granting entry, preventing unauthorized or improperly equipped personnel from reaching high-risk areas.

  3. PPE Training and Assessment: DPI_detection can be used to create interactive training modules and assessment tools for PPE education initiatives. The model can provide instant feedback to trainees on their PPE usage, helping them understand the importance of wearing necessary equipment and how to use it properly.

  4. Augmented Reality Assistance: The model can be incorporated into an augmented reality (AR) system, providing real-time guidance for users on their PPE usage. For instance, an AR helmet could overlay visual cues about the correct PPE to wear in specific work zones, reinforcing safe practices and improving overall workplace safety.

  5. PPE Inventory Management: By identifying various DPI classes, DPI_detection can be used in conjunction with RFID or other tracking technologies to help manage the inventory of PPE. This would assist in real-time monitoring of equipment availability, ensuring that employees always have access to the required PPE and enabling quick replenishment when necessary.

Use Free Caschetto, Cuffie and Gilet Detection API

Powered by general detection model

Code
pip install inference-sdk
# 1. Import the library
from inference_sdk import InferenceHTTPClient

# 2. Connect to your workspace
client = InferenceHTTPClient(
  api_url="https://serverless.roboflow.com",
  api_key="API_KEY"
)

# 3. Run your workflow on an image
result = client.run_workflow(
  workspace_name="<YOUR_WORKSPACE>",
  workflow_id="<YOUR_WORKFLOW_ID>",
  images={
    "image": "YOUR_IMAGE.jpg"  # Path to your image file
  },
  parameters={
    "classes": "Caschetto, Cuffie, Gilet, No Caschetto, No Cuffie"
  },
  use_cache=True  # cache workflow definition for 15 minutes
)

# 4. Get your results
print(result)

Run on custom image

Drop an image here or click to upload

Detecting classes:
Or try a test image 

Cite This Project

LicenseCC BY 4.0

If you use this dataset in a research paper, please cite it using the following BibTeX:

@misc{ dpi_detection_dataset,
  title = { DPI_detection Dataset },
  type = { Open Source Dataset },
  author = { Nadia },
  howpublished = { \url{ https://universe.roboflow.com/nadia-modmb/dpi_detection } },
  url = { https://universe.roboflow.com/nadia-modmb/dpi_detection },
  journal = { Roboflow Universe },
  publisher = { Roboflow },
  year = { 2023 },
  month = { may },
  note = { visited on 2026-07-29 },
}

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