trainpd Computer Vision Project

trainpdv2

Updated a year ago

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thick crack
thin crack

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Description

Here are a few use cases for this project:

  1. Infrastructure Assessment: "trainpd" can be used by civil engineers and infrastructure authorities to automate the process of inspecting buildings, bridges, roads, and tunnels, effectively identifying types of cracks (thick or thin) that can indicate deterioration or damage.

  2. Earthquake Damage Analysis: The model can be implemented in analyzing aerial or satellite images post-earthquake, identifying the scale and type of damage in terms of cracks on the ground. This helps in disaster management planning, enabling quick responses to the most severely affected areas.

  3. Archaeological Site Preservation: The identification of crack types in ancient structures and monuments can help archaeologists and restorers to understand the severity of decay and take necessary measures for preservation.

  4. Mining Safety: Mining companies can utilize the model to inspect and evaluate mine stability by identifying and classifying cracks, therefore enhancing worker safety and preventing potential accidents.

  5. Real Estate Inspection: Property inspectors could use "trainpd" to identify structural issues in residential and commercial buildings more efficiently. This assists in providing more thorough reports to potential buyers or lessees, ensuring building safety and adherence to regulations.

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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{
                            trainpd_dataset,
                            title = { trainpd Dataset },
                            type = { Open Source Dataset },
                            author = { trainpdv2 },
                            howpublished = { \url{ https://universe.roboflow.com/trainpdv2/trainpd } },
                            url = { https://universe.roboflow.com/trainpdv2/trainpd },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2023 },
                            month = { apr },
                            note = { visited on 2024-09-23 },
                            }
                        
                    

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