Automating Utility Pole Recognition & Inspection with Computer Vision

Published by Drew Clancy on

Computer Vision Intro

Computer vision offers significant cost savings by way of scalability and automation. Collecting a large set of image data is only the first step. The true art of any AI application comes down to how a data scientist can tune AI to accurately label what is in the image set. Mosaic Data Science is poised to help companies make these deep learning techniques work for them and, in the following case study, did just that.

Need for Computer Vision

One of the largest utilities in the United States needed an AI consulting partner to deploy computer vision effectively & efficiently. The company uses a fleet of drones to inspect some of their physical assets and had collected a large data set of inspection images. To maximize their return on this investment, the firm needed deep learning models to find and identify different equipment types in the images and, eventually, to help diagnose visible defects to alert their personnel to a maintenance need. What good is a set of images if a machine cannot learn and provide recommendations to operational decision makers? The company approached Mosaic to assist with this effort.

Mosaic, a leading AI consulting company, was tasked to design computer vision models that automatically identify and label various asset types in inspection images. The models would be integrated with an image inspection tool to enable analysts to quickly search for images of specific equipment types such as pole tops, crossarms, insulators, and transformers; automatically catalog the equipment installed on the pole; and potentially flag defects for closer inspection. A human-in-the-loop feedback mechanism would be implemented to allow any annotations that were approved or adjusted by analysts during the review process to be added to the training data to continuously improve model performance. The utility needed this to be completed in a span of 3 months.

A deep learning process diagram visualizing the steps necessary to deploy computer vision correctly.
Data Annotation and Image Processing

Obtaining a sufficiently large sample of good quality data – accurately annotated images – is a crucial and often challenging first step in all computer vision projects. In this case, drone-captured images were being stored on Azure blob storage but did not have image annotations. Mosaic worked with the utility customer to guide the image annotation process. After a comparison of a variety of annotation platforms and an analysis of the customer’s current and future annotation needs, VGG Image Annotator (VIA) was chosen for the project based on ease of installation/