Anomali pada pipa Computer Vision Project
Updated 2 years ago
Metrics
Here are a few use cases for this project:
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Pipeline Monitoring and Maintenance: Use the "Anomali pada pipa" model to regularly monitor the condition of both underground and overground pipelines across various industries (like gas, oil, water, and sewage). Spotting anomalies such as corrosion, isolated cracks, or crack colonies can dramatically cut maintenance costs and prevent disastrous leaks before they happen.
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Infrastructure Safety: The model can be utilized by city or highway authorities to monitor for structural weaknesses in concrete structures such as bridges, tunnels, or roads. By detecting early signs of damage, they can proactively repair these key infrastructures to avoid larger future damages.
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Construction Quality Control: The model can be used in construction sites for assessing the quality of concrete structures during and after construction. This ensures all work complies with safety standards and potentially increases the lifespans of these structures.
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Historical Monument Preservation: Heritage conservation organizations can employ this technology to spot anomalies such as cracks on exquisitely constructed ancient monuments or buildings. This aids them in their efforts to maintain and restore these historical structures, while minimizing further damage.
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Deep Sea Infrastructure Monitoring: Oil and gas companies could apply the model to monitor deep sea pipelines and structures, detecting any signs of corrosion or cracks in the extreme conditions of the ocean floor. This could prevent costly and environmentally damaging leaks.
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Cite This Project
If you use this dataset in a research paper, please cite it using the following BibTeX:
@misc{
anomali-pada-pipa_dataset,
title = { Anomali pada pipa Dataset },
type = { Open Source Dataset },
author = { Anomaly inspection labelling },
howpublished = { \url{ https://universe.roboflow.com/anomaly-inspection-labelling/anomali-pada-pipa } },
url = { https://universe.roboflow.com/anomaly-inspection-labelling/anomali-pada-pipa },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { dec },
note = { visited on 2024-11-16 },
}