Universe

yolo Computer Vision Dataset

bykyneaTask:
Object Detection
License:Public Domain642 views25 downloads
See all 78 imagesyolo weldingdefects sample showing D, SI, TE, TEEyolo weldingdefects sample showing BE, SI, TE, TEEyolo weldingdefects sample showing BE, D, SI, TEyolo weldingdefects sample showing BE, D, SI, TE, TEEyolo weldingdefects sample showing D, SI, TEyolo weldingdefects sample showing BE, SI, TE, TEEyolo weldingdefects sample showing D, SI, TE, TEEyolo weldingdefects sample showing BE, SI, TEyolo weldingdefects sample showing BE, D, SI, TEyolo weldingdefects sample showing BE, D, SI, TE, TEEyolo weldingdefects sample showing BE, D, SI, TE, TEEyolo weldingdefects sample showing BE, D, SI, TE

About yolo Dataset

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.

Use Free BE, D and SI 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": "BE, D, SI, TE, TEE"
  },
  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

LicensePublic Domain

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{ https://universe.roboflow.com/kynea/yolo-nryid } },
  url = { https://universe.roboflow.com/kynea/yolo-nryid },
  journal = { Roboflow Universe },
  publisher = { Roboflow },
  year = { 2022 },
  month = { apr },
  note = { visited on 2026-07-29 },
}

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