Sewer Line Image Inspection With Hierarchical ML Classification
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Solution Overview
Problem
Manual inspection of sewer lines is time-consuming and prone to human error, leading to inaccurate identification and categorization of characteristics, which can result in financial losses due to inefficient manpower use and incorrect maintenance decisions.
Innovation Solution
A system utilizing machine learning, specifically convolutional neural networks, to automatically analyze images of sewer lines and identify and categorize specific characteristics, including the use of a hierarchical classification approach for accurate prediction and recommendation of maintenance needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual inspection by analysts is used, then identification and categorization of sewer characteristics can be performed, but the process is time-consuming and results in financial loss due to inefficient manpower use
Solution Approach 1:
The patent replaces the manual mechanical inspection process with an automated machine learning system. The ML model processes sewer inspection images automatically, substituting human analysts with computational algorithms that perform identification and categorization without manual intervention, thereby eliminating time loss while maintaining accuracy.
Solution Approach 2:
The system enables self-service inspection where the machine learning model autonomously performs the entire inspection workflow including image processing, characteristic identification, and categorization without requiring human analysts. The system serves itself by automatically generating inspection reports and maintaining the inspection database.
2Reliability
If manual inspection by analysts is used, then sewer line characteristics can be identified and categorized, but human error leads to inaccurate identifications and categorization
Solution Approach 1:
The patent replaces human analysts with machine learning algorithms that eliminate human error. The automated system consistently applies the same classification criteria without fatigue, distraction, or subjective interpretation, ensuring reliable and precise identification and categorization of sewer characteristics.
Solution Approach 2:
The system incorporates feedback mechanisms where inspection results are continuously processed and used to improve future inspections. The machine learning model learns from accumulated data, refining its accuracy over time and ensuring consistent reliable performance without the variability inherent in human inspection.
3Productivity
If automated machine learning inspection is implemented, then inspection time and cost are reduced, but system complexity increases
Solution Approach 1:
The machine learning system performs multiple functions within a single integrated platform: image processing, characteristic identification, categorization, and report generation. This multi-functional approach consolidates what would otherwise require multiple separate systems, managing complexity while maximizing productivity benefits.
Solution Approach 2:
The inspection system is segmented into modular components including the ML model, database management, image processing modules, and report generation. This segmentation allows each component to be independently optimized and maintained, managing overall system complexity while enabling high productivity through coordinated operation of specialized modules.
Data Source
AI summary
A system for performing automated inspection of a sewer line using machine learning. The system comprises a inspection data database storing sewer inspection data including images and metadata associated therewith; an inspection upload module receiving and uploading the sewer inspection data to the inspection data database; and an inspection module receiving the sewer inspection data from the inspection data database and generating therefrom identification data including characteristics of the sewer line identified and categorized. The identification module uses at least one machine learning model processing the sewer inspection data and determining whether at least one sewer specific characteristic is identifiable in the images and categorizing the at least one sewer specific, using a plurality of hierarchical classes where a top class performs the determination of whether the at least one sewer specific characteristic is present and the identification thereof and lower classes perform categorization in decreasing abstraction levels.