Pipeline Fault Classification Using Multi-Label Image Inspection
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Solution Overview
Problem
Existing methods for identifying defects in underground pipelines, particularly wastewater networks, are labor-intensive, prone to operator subjectivity, and require specialized equipment, failing to accurately classify multiple defects simultaneously and lacking real-world applicability.
Innovation Solution
A statistical learning model, such as a multi-label classifier with deep convolutional neural networks, is used to identify multiple defects in pipeline photographs, providing operator decision support and improving report accuracy by classifying defects based on conventional inspection processes without additional training or equipment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual defect identification by operators is used, then defect classification can be performed, but the process is labor-intensive, time-consuming, and prone to operator subjectivity
Solution Approach 1:
The patent replaces the manual mechanical inspection process with an automated image processing system that uses computer vision algorithms to detect and classify defects. The system automatically processes inspection images, identifies defect types, and generates classification results, eliminating the need for operators to manually examine and categorize each defect, thereby reducing inspection time while maintaining or improving classification accuracy
Solution Approach 2:
The system enables self-service defect identification by using artificially intelligent algorithms that automatically analyze inspection images and classify defects without human intervention. The machine learning models are trained to recognize various defect patterns and can independently perform classification tasks, making the inspection process autonomous and eliminating operator subjectivity
2Reliability
If special equipment such as fisheye lenses and 3D reconstruction systems are used, then comprehensive pipeline inspection is achieved, but equipment complexity and operator training requirements increase
Solution Approach 1:
The patent extracts and eliminates the need for complex special-purpose equipment by using standard imaging devices combined with advanced image processing algorithms. Instead of relying on fisheye lenses and 3D reconstruction hardware, the system processes standard inspection images through artificially intelligent algorithms that can identify and classify multiple defect types, thereby maintaining inspection reliability while dramatically reducing equipment complexity
Solution Approach 2:
The system creates a digital representation of defects through image processing rather than physical 3D reconstruction. By analyzing two-dimensional inspection images with computer vision algorithms, the system generates accurate defect classifications without requiring complex optical equipment or specialized hardware, thus simplifying the overall inspection system while maintaining comprehensive defect detection capabilities
3Extent of automation
If existing automated solutions are used, then some defect types can be detected, but they are limited to a small number of defect types and cannot simultaneously classify multiple defects in the same photograph
Solution Approach 1:
The patent implements a universal defect classification system that can simultaneously identify and classify multiple types of defects within a single image. The artificially intelligent algorithm is designed to detect various defect types including cracks, deposits, root intrusions, and other pipeline deficiencies in one comprehensive analysis process, eliminating the need for multiple specialized detection systems and enabling versatile defect type coverage across different pipeline conditions
Data Source
Figure 1~2
AI summary
A pipeline fault identification system to facilitate the determination of the type of faults present in such a pipeline, and a method for training a statistical learning model for such use and a method for identifying faults in a pipeline, the identification system comprising a robot (40), equipped with a locomotion device (41) and a camera (42), configured to travel through the pipeline and take photographs of portions of the pipeline suspected of having one or more faults, and a processing system (20), equipped with a statistical learning model (22) configured to determine, on the basis of such a photograph, the type of each fault recognized in the photograph from a pre-established list of at least 10 types of faults.