Utility Pole Maintenance Automation | Vision intelligence based ML platform |

Business challenges

Health and safety risks: Climbing and inspecting utility poles can be dangerous and poses health risks to workers, including the risk of falls and electrical hazards.

Time-consuming and labor-intensive: Inspecting utility poles by hand can be a slow and physically demanding process, leading to long inspection times and high labor costs.

Inaccurate results: Manually inspecting poles can lead to inconsistent and inaccurate results, as it is difficult to ensure that every pole is inspected thoroughly and with the same level of detail.

Limited coverage: Manually inspecting poles can only be done in certain areas that are easily accessible, meaning that remote or difficult-to-reach poles may not be inspected.

High maintenance costs: The cost of maintaining and replacing equipment used in manual inspections can be high, particularly if the equipment is damaged or worn out from regular use.

Talk To Our Experts!

Solution Overview

Our client, a Denver-based company, is a leader in bringing automation to utility pole maintenance in the US.

Their goal is to reduce manual attention to utility poles across Denver and improve workforce efficiency.

We have developed a LOW-CODE-NO-CODE platform that automates the entire machine learning process, from data annotation to model deployment, through a simple and easy-to-use web-based portal.

Advances in vision intelligence have allowed us to create a DIY machine learning platform where users can upload utility pole images, tag components, and train segmentation algorithms.

This algorithm assists the inspection team in automating much of the inspection process and allow for accurate measurements and audit report generation.

Business Impact

Increased efficiency and productivity: AI-based inspection systems can inspect poles faster and more accurately than manual inspection, reducing inspection times and increasing productivity.

Improved safety: Automated inspection systems can reduce the need for workers to climb utility poles, reducing health and safety risks and increasing worker safety.

Better accuracy: AI-based inspection systems can provide consistent and accurate results, reducing the likelihood of missed issues and improving the overall quality of the inspection process.

Data analysis and reporting: Automated inspection systems can collect and analyze large amounts of data, providing valuable insights and enabling utilities to make informed decisions.

Cost savings: The increased efficiency, improved accuracy, and reduced maintenance costs of automated inspection systems can result in significant cost savings for utilities.

Talk To Our Experts!

Technology Stack

Business Challenges

Utility inspection is an expensive and labor-intensive process. A human maintenance engineer, physically visits or reviews, videos/images of the utility pole to take measurements and perform audits.

Key Features

Automating Utility Pole inspection using Vision Intelligence is a key differentiator amongst their competitors.

This automation has offered tremendous value, cost-saving, and ROI for the client compared to the manual inspection, which had been the industry standard.

The inspection data can be uploaded, tagged and Image segmentation models can be trained by non-technical team members easily in the web-based portal.

Trained models can be deployed in on-premise or cloud infrastructure.

Trusted and Proven Engagement Model

The solution discovery phase is all about knowing your target audience, writing down requirements, and creating a full scope for the project.
This helps clarify the goals, and limitations, and deliver quality products & services.

A nondisclosure agreement (NDA) is signed to not disclose any sensitive information revealed over the course of doing business together.
Our NDA-driven process is established to keep clients’ data and IP safe and secure.

Our engagement model defines the project size, project development plan, duration, concept, POC etc.
Based on these scenarios, clients may agree to a particular engagement model (Fixed Bid, T&M, Dedicated Team).

The SOW document shall list details on project requirements, project management tools, tech stacks, deliverables, milestones, timelines, team size, hourly/monthly rate cards, billable hours and invoice details.

On signing the SOW, an official project kick-off meeting shall be initiated.
Our implementation approach, ecosystem, tools, solutions modelling, sprint plan, etc. shall be discussed during this meeting.

Our Award-Winning Team

A seasoned AI & ML team of young, dynamic and curious minds recognized with global awards for making significant impact on making human lives better

Awarded Bronze Trophy at CII National competition on Digitization, Robotics & Automation (DRA) – Industry 4.0

5yrs

in AI & ML
Engineering

40+

AI & ML
Projects for
reputed Clients

50+

AI & ML
Engineers

Awarded as Winner among 1000 contestants at TechSHack Hackathon

Related capabilities

On Demand Machine Learning Model Deployment In AWS

An ergonomic assessment also called an ergonomic risk assessment, is an objective measure of the risk factors in your work environment that may lead to musculoskeletal disorders or injuries among your workforce, so that you can make measurable improvements in the work environment.