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 Transmission H-Structure and its different parts. 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 1, 2025 Number of downloads: 1,427

Snapshot last refreshed:

1969 characters left.

Description

Electricity might seem ubiquitous but behind its omnipresence is a large infrastructure. The power generated in power plants is transmitted over long distances on conductor wires and towers. These towers vary in shape and design and are built up of different components. One such structure is an H-frame. These structures can get damaged over time and its various components have unique degradation modes. Identifying these damages can help in prioritizing maintenance and repairs. This can prevent loss of energy, further damage to the transmission infrastructure and disruption of supply.
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 on the ground. The images collected are then manually analyzed. 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 H-frame structures and its different parts for further automated analysis.


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 oriented imagery of transmission h-structures.


Output

Feature class representing detected H-structure and its parts. 


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
h_structure 0.94
pole 0.94
crossarm 0.96
insulator 0.89
x_brace 0.90


Training data

This model has been trained on the Transmission H-frame Dataset 1.0 by Electric Power Research Institute (EPRI).


Sample results

Here are a few results from the model.


Result


Result



Citations 
Transmission H-frame dataset 1.0. EPRI, P. Kulkarni, D. Lewis. 2022. Kaggle. CC BY-SA 4.0. Available here.


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 (2)

Sign in to add a comment.

image.analyst.eu Item Owner commented 2 years ago Delete Reply

Please double check the link to the guide

esri_analytics Item Owner commented 2 years ago Delete

The link has been corrected - thank you for reporting this.

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: 222.934 MB

          Attachments size: 0 KB

          ID: 8f41292ba13e448281021028a80c6df0

          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

          dlpk, LivingAtlasDLPK, utilities, h-structure, hframe, Transmission H-Structure Detection, pretrained model, Deep Learning Package, electrical transmission

          Edit Tags
          Credits (Attribution)
          No acknowledgements.

          EPRI, 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…