Pipe Anomaly Detection With ML Video Alerts and GPS Mapping

Solution Overview

Problem

Conventional conduit inspections for wastewater and sewage systems are labor-intensive and prone to human error, with operators spending significant time reviewing video streams to locate anomalies, leading to inefficiencies and potential missed detections due to visibility issues and lack of precise location data.

Innovation Solution

An optical imaging system equipped with a machine learning protocol that automatically detects conduit anomalies by analyzing video streams, applying alerts, and recording precise locations using GPS, enabling real-time detection and mapping of anomalies.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If manual review of video streams is performed by operators and technicians, then conduit anomalies can be detected, but labor time and inspection costs increase significantly

Engineering Contradiction:

Improveanomaly detection accuracyVSAvoidinspection time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The patent replaces the manual mechanical review process with an automated computer-based system that captures video streams from conduit inspections and automatically processes them to identify anomalies. The system uses software algorithms to detect fissures, cracks, roots, and other structural issues without requiring human operators to manually review footage, thereby eliminating labor time while maintaining detection accuracy.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Reliability

If manual review of video streams is performed by operators and technicians, then conduit anomalies can be detected, but human errors occur and anomalies may be missed

Engineering Contradiction:

Improveanomaly detection accuracyVSAvoidinspection process complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The patent replaces the manual mechanical review process with an automated computer-based system that captures video streams from conduit inspections and automatically processes them to identify anomalies. The system uses software algorithms to detect fissures, cracks, roots, and other structural issues without requiring human operators to manually review footage, thereby eliminating labor time while maintaining detection accuracy.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Measurement precision

If travel distance measurement is used to locate anomalies, then a single set of data points is obtained, but precise location identification becomes difficult when conduits are not visible

Engineering Contradiction:

Improveanomaly location precisionVSAvoidconduit route information

Core Design Contradiction:

Measurement precisionVSLoss of information

Solution Approach 1:

The patent enhances the basic travel distance measurement by integrating GPS technology and creating visual representations of conduit routes. The system plots conduit paths on maps and marks anomaly locations with flags and coordinates, adding spatial dimensionality to the data. This allows operators to locate anomalies even when conduits are buried or not visible, by referencing their plotted positions on the visual route maps.

Inventive Principle:

Principle #17Another dimension (Dimensionality change)

Data Source

PatentUS20250232423A1Method and apparatus for locating anomalies in a pipe

Publication Date: 2025.07.17 HYDROMAX USA LLC

AI summary

A method for automatically detecting at least one anomaly inside of a conduit. The method includes steps of: moving an optical imaging device of a system inside of the conduit; viewing at least one anomaly inside of the conduit with the optical imaging device; outputting a video stream by the optical imaging device with the at least one anomaly to a user interface of the system; executing an anomaly detection program, by a controller of the system, from a computer readable medium in response to the at least one anomaly being viewed by the optical imaging device, wherein the controller is caused to: automatically detect the at least one anomaly with a machine learning protocol of the anomaly detection program; and apply an alert to the at least one anomaly on the video stream.