How can a city, utility or road maintenance team use computer vision to spot damaged, missing or sunken manhole covers and utility lids along its streets?

Cameras on a maintenance truck or a crew phone capture the street surface in a steady drive-by, and the model marks covers and lids that may need a closer look, such as missing, cracked or broken covers, lids that sit too high or too low, rocking or shifted covers, and broken pavement or rings around them. Each mark is saved with the image, the asset ID or GPS spot and the date, so crews can plan repairs by priority. A qualified utility or road crew lead decides every job, and the usual inspections, standards and records stay in place.

What this means in practice

Cameras on a maintenance or patrol truck, or a crew phone, capture the street surface in a steady drive-by, ideally on the same lanes, speed and settings each pass.
The model is trained on the owner own street images. It marks missing, cracked or broken manhole covers, valve boxes and utility lids, covers that sit too high or too low compared with the road, shifted or tilted covers, and cracked, sunken or broken pavement and rings around them.
Each mark is saved with the image, the asset ID or GPS spot and the date, so the team can compare against the last pass, see which covers keep coming back and plan repairs by priority, with missing or open covers sent straight to the crew lead.
A qualified utility or road crew lead reviews every mark and decides what gets fixed, replaced or left as is. Photos only show the top of the cover, so what is underneath still needs hands-on checks, and any people or plates caught in view are blurred.
Start with one district or route, compare the marks with what crews find on their own rounds for a few weeks, and keep the existing inspections, safety rules, standards, work orders and records in place. Camera checks add to those inspections and never replace them.

How Lid Vizion approaches it

We start with the business decision and operating context, define ownership and measurable milestones, then use configuration, integrations, automation, or custom software only where each earns its place.

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---
title: "How to check manhole covers and utility lids with computer vision"
description: "Cameras on a maintenance truck or a crew phone capture the street surface in a steady drive-by, and the model marks covers and lids that may need a closer look, such as missing, cracked or broken covers, lids that sit too high or too low, rocking or shifted covers, and broken pavement or rings around them. Each mark is saved with the image, the asset ID or GPS spot and the date, so crews can plan repairs by priority. A qualified utility or road crew lead decides every job, and the usual inspections, standards and records stay in place."
url: https://lidvizion.ai/answers/how-to-check-manhole-covers-and-utility-lids-with-computer-vision/
site: Lid Vizion
index: https://lidvizion.ai/llms.txt
---

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# How can a city, utility or road maintenance team use computer vision to spot damaged, missing or sunken manhole covers and utility lids along its streets?

Cameras on a maintenance truck or a crew phone capture the street surface in a steady drive-by, and the model marks covers and lids that may need a closer look, such as missing, cracked or broken covers, lids that sit too high or too low, rocking or shifted covers, and broken pavement or rings around them. Each mark is saved with the image, the asset ID or GPS spot and the date, so crews can plan repairs by priority. A qualified utility or road crew lead decides every job, and the usual inspections, standards and records stay in place.

## What this means in practice

- Cameras on a maintenance or patrol truck, or a crew phone, capture the street surface in a steady drive-by, ideally on the same lanes, speed and settings each pass.
- The model is trained on the owner own street images. It marks missing, cracked or broken manhole covers, valve boxes and utility lids, covers that sit too high or too low compared with the road, shifted or tilted covers, and cracked, sunken or broken pavement and rings around them.
- Each mark is saved with the image, the asset ID or GPS spot and the date, so the team can compare against the last pass, see which covers keep coming back and plan repairs by priority, with missing or open covers sent straight to the crew lead.
- A qualified utility or road crew lead reviews every mark and decides what gets fixed, replaced or left as is. Photos only show the top of the cover, so what is underneath still needs hands-on checks, and any people or plates caught in view are blurred.
- Start with one district or route, compare the marks with what crews find on their own rounds for a few weeks, and keep the existing inspections, safety rules, standards, work orders and records in place. Camera checks add to those inspections and never replace them.

## How Lid Vizion approaches it

We start with the business decision and operating context, define ownership and measurable milestones, then use configuration, integrations, automation, or custom software only where each earns its place.

## More on Automation & custom AI

- [How can a water utility or fire department use computer vision to spot fire hydrant problems between hands-on inspections and flow tests?](https://lidvizion.ai/answers/how-to-check-fire-hydrants-with-computer-vision/)
- [How can a road agency or highway maintenance team use computer vision to check guardrails, crash barriers and their end treatments along a route?](https://lidvizion.ai/answers/how-to-check-guardrails-and-crash-barriers-with-computer-vision/)
- [How can a parks department, school or property manager use computer vision to check playground equipment between hands-on inspections?](https://lidvizion.ai/answers/how-to-check-playground-equipment-with-computer-vision/)

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