Loading…
This layer is editable and shared with the public. To prevent unwanted editing, unshare this item or approve it for public data collection.
Finish setting up your layer
Describe your item below. Add fields on the Data tab. Configure editing on the Settings tab. Configure drawing and pop-ups through Map Viewer or Visualization tab.
Open
Creating Service

Deep Learning model to detect insulators and classify defects. A brief summary of the item is not available. Add a brief summary about the item.

‎Deep learning package by

New notebook runtime available. You can update the runtime from the settings tab of the item details page.

Item created: May 18, 2023 Item updated: Jan 2, 2025 Number of downloads: 1,632

Snapshot last refreshed:

1986 characters left.

Description

Electric transmission towers use insulators to prevent the leakage of current from the conductors to the ground. These transmission lines carry electricity at very high voltages which sometimes damages the insulators. A flashover damage happens when the current passes through the air gaps between the insulators. The insulators can also break due to the passage of high-voltage current through the insulator body or sometimes due to mechanical load. Identifying such defects in these insulators can help in prioritizing maintenance and repairs. This can prevent loss of energy and further damage to the transmission infrastructure.
Power corporations perform regular inspections of transmission and distribution infrastructure. To perform these inspections images of transmission assets are collected using helicopter, drones or sometimes from vehicles or people carrying instruments on the ground. The images collected are then manually checked to identify any defects in these assets. An inspection flight can generate hundreds of images in just one mile. There are thousands of miles of transmission lines that run across the length and breadth of a country. Manually checking each image can be a tedious task. This model can be used to automate the task of detecting defects in insulators.


Using the model

Follow the guide to use the model. Before using this model, ensure that the supported deep learning libraries are installed. For more details, check Deep Learning Libraries Installer for ArcGIS.


Fine-tuning the model
This model can be fine-tuned using the Train Deep Learning Model tool. Follow the guide to fine-tune this model.

Input

8-bit, 3-band high-resolution imagery of insulator strings.


Output

Feature class representing detected and classified insulators. 


Applicable geographies

The model is expected to work well in the United States.


Model architecture

This model uses the MMDetection-reppoints model implemented in ArcGIS API for Python.


Accuracy metrics

The table below summarizes the average precision of the model on the validation dataset.


Class Average Precision
Broken 0.92
Flashover damage 0.86
No issues 0.72
String 0.99


Training data

This model has been trained on the Insulator Defect Image Dataset (IDID).


Limitations

This model will work well with high resolution close-up images of porcelain insulator strings. However, results can vary for imagery that are statistically dissimilar to training data.


Sample results
Here are a few results from the model.


Result


Result



Citations
Dexter Lewis, Pratik Kulkarni, August 11, 2021, "Insulator Defect Detection", IEEE Dataport, doi: https://dx.doi.org/10.21227/vkdw-x769.s


An in-depth description of the item is not available.

Layers

Ground Layers

Tools

Tables

Basemap

Project Contents:

Solution Contents

Contents

Layers

Screenshots

Terms of Use

https://downloads.esri.com/blogs/arcgisonline/esrilogo_new.png This work is licensed under the Esri Master License Agreement.

No special restrictions or limitations on using the item's content have been provided.

Comments (4)

Sign in to add a comment.

arcgishld Item Owner commented 2 years ago Delete Reply

Dear ESRI Could you please access the document called "guide to use the model" of Deep Learning model to detect insulators and classify defects?

esri_analytics Item Owner commented 2 years ago Delete

The link to the guide has been updated. Please refer it to use the model.

fmillet@esri.ca Item Owner commented 2 years ago Delete Reply

Hello, In the section "Using the model", the link to the "guide" doesn't work (error page). Can it be fixed? Thanks :)

esri_analytics Item Owner commented 2 years ago Delete

The link to the guide has been updated. Please refer it to use the model.

Item Information

LowHigh

Item Information

LowHigh

Make your item easy to find, understand, and use by providing this information.

    Details

    Dashboard views: Desktop

    Source: (Image Collection)

    Creating data in:

    Published as:

      Other Views:

        Dependent items in the recycle bin

          Applicable: 2d

          Size: 223.278 MB

          Attachments size: 0 KB

          ID: 5e9466b362b741ef84eb7b8ba726986c

          Image Count: 0

          Image Properties

          Layer Drawing

          Using tiles from a cache

          Dynamically from data

          Share
          Owner

          esri_analytics

          Folder

          Categories

          This item has not been categorized.

          Assign Category
          Tags

          LivingAtlasDLPK, dlpk, Object Detection, insulator defect, pretrained model, utilities, electrical transmission

          Edit Tags
          Credits (Attribution)
          No acknowledgements.

          Esri

          ArcGIS Navigator link
          URL View
          WMTS View
          Your tile layer is ready to use
          This tile layer will automatically create tiles as needed and cache them for future use. No further configuration is required. View the Settings tab to change the default options. Build tiles manually for specific scales and extents to improve display performance for the first person to view the tile layer at that scale and extent. Tiles must exist if the layer will be used offline.
          All items were exported successfully
          ${numberOfItems} item(s) were exported successfully. Some item(s) skipped or failed to export.
          See description for more information
          Cannot import
          Export packages from newer portal versions cannot be imported to older versions.
          Loading…