Object Detection

yolo Computer Vision Project


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Here are a few use cases for this project:

  1. Industrial Quality Control: YOLO can be used in manufacturing industries to automate the process of identifying and categorizing welding defects. This can help maintain high-quality standards, reducing the rate of defective products, and minimizing waste in the production process.

  2. Automated Welding Repair: The model can be integrated into robotic or automated welding systems to identify defects during the welding process and perform immediate repair or adjustments, ensuring that the final product meets the desired specifications.

  3. Training and Education: The computer vision model can be used to develop an interactive educational tool to teach new welders, inspectors, or technicians about various types of welding defects and their classifications, allowing them to identify and correct issues in real-world scenarios.

  4. Predictive Maintenance: YOLO can assist in identifying patterns of defects in large-scale infrastructure projects, such as bridges, pipelines, or buildings, allowing engineers to address potential issues proactively and extend the life of these structures.

  5. Augmented Reality for Welders: The model can be combined with augmented reality (AR) technology to provide welders with real-time feedback about the quality of their work, helping them improve their skills and prevent the formation of defects in the first place.

Cite this Project

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

@misc{ yolo-nryid_dataset,
    title = { yolo Dataset },
    type = { Open Source Dataset },
    author = { kynea },
    howpublished = { \url{ } },
    url = { },
    journal = { Roboflow Universe },
    publisher = { Roboflow },
    year = { 2022 },
    month = { apr },
    note = { visited on 2023-11-20 },

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Last Updated

2 years ago

Project Type

Object Detection





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Public Domain