DBIS2022_P4 Computer Vision Model
How to use the DBIS2022_P4 Detection API
Try This Model
Drop an image here or click to upload
Code Snippets
from inference_sdk import InferenceHTTPClient
CLIENT = InferenceHTTPClient(
api_url="https://serverless.roboflow.com",
api_key="API_KEY"
)
result = CLIENT.infer("YOUR_IMAGE.jpg", model_id="dbis2022_p4/3")Or, Use Free Plate, Insulator and Insulator Detection API
Powered by general detection model
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": "plate, Insulator, insulator, transformer, tower"
},
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
About DBIS2022_P4 Model
Here are a few use cases for this project:
-
Grid Health Monitoring: The DBIS2022_P4 model can be used in power grid systems to automatically identify and monitor the condition of different components such as insulators, transformers, cables, etc. for damage or faults. This could significantly reduce the time and cost involved in manual inspections.
-
Predictive Maintenance in Power Plants: The model can be used to predict possible failure points and schedule maintenance based on subtle changes in insulators or other components. This will increase safety and efficiency in power plants.
-
Hardware Quality Control: Companies manufacturing power equipment can use the model to inspect their products (cables, insulators, transformers) for quality assurance purposes, identifying defective or sub-standard products.
-
Damage Assessment during Natural Disasters: The model can assist in doing swift and accurate damage assessment of power infrastructure after natural disasters such as hurricanes and earthquakes, helping in prioritizing repairs and restoration of normal power supply.
-
Electrician Training: The model could serve as a training aid for new electricians or electrical engineers, helping them understand different components and identify common issues like a bent insulator, frayed cable, or burned transformer.
Tell the agent what you want to build.
Cite This Project
LicenseCC BY 4.0If you use this dataset in a research paper, please cite it using the following BibTeX:
@misc{ dbis2022_p4_dataset,
title = { DBIS2022_P4 Dataset },
type = { Open Source Dataset },
author = { max kellerman },
howpublished = { \url{ https://universe.roboflow.com/max-kellerman-cz98k/dbis2022_p4 } },
url = { https://universe.roboflow.com/max-kellerman-cz98k/dbis2022_p4 },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2023 },
month = { mar },
note = { visited on 2026-07-29 },
}