Train Computer Vision Project

SK

Updated 2 years ago

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Classes (3)
heavy
safety
worker
Description

Here are a few use cases for this project:

  1. Safety Compliance Monitoring: The model can be used to monitor safety compliance in construction or mining sites. By identifying workers, heavy machinery, and safety objects like helmets and protective gear, the model can ensure all safety protocols are being followed.

  2. Automated Inventory Management: Construction and industrial companies can use this model to keep a track of heavy machinery and equipment. The computer vision system can monitor and identify different classes of heavy equipment like bulldozers, cranes, dump trucks, etc., to automate inventory management.

  3. Risk Assessment in Construction Sites: The "Train" model can be used for comprehensive risk assessment on construction sites, identifying potential risks related to worker safety, machinery operation, and more.

  4. Training Simulation Analysis: The model can be integrated into training simulations to provide real-time feedback. For example, trainees operating heavy machinery in a simulated environment can have their actions monitored and analysed by the model.

  5. Incident Reporting: When an incident occurs on a site, the model can help identify what happened by analysing pre-incident images. For example, it can identify if a worker was near a bulldozer before an accident, or if safety equipment was not being worn appropriately.

Supervision

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Cite This Project

LICENSE
CC BY 4.0

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

                        @misc{
                            train-aie3x_dataset,
                            title = { Train Dataset },
                            type = { Open Source Dataset },
                            author = { SK },
                            howpublished = { \url{ https://universe.roboflow.com/sk-kjvib/train-aie3x } },
                            url = { https://universe.roboflow.com/sk-kjvib/train-aie3x },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2023 },
                            month = { apr },
                            note = { visited on 2024-12-03 },
                            }