Inspection Tool Automatic Feature Detection Classification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current inspection tools lack an efficient method to classify and label environmental aspects, such as pipe conditions and defects, in real-time using machine learning, which limits their accuracy and automation capabilities.
Innovation Solution
The integration of a machine learning classifier within inspection tools that processes data from various sensors, including cameras, to identify and label pipe conditions, defects, and other environmental features, utilizing algorithms like artificial neural networks and computer vision techniques, enabling real-time classification and feedback mechanisms for improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual labeling methods are used for inspection data, then users can input labels via display or monitor, but the inspection efficiency and automation capability are limited
Solution Approach 1:
The patent replaces the manual mechanical labeling process with an automated machine learning classification system. The ML classifier automatically processes sensor data and assigns labels to inspection features, eliminating the need for manual user input and significantly improving inspection efficiency while enhancing automation capability.
Solution Approach 2:
The inspection system performs self-service through the integrated ML classifier that automatically classifies and labels inspection data without requiring external user intervention. The system serves itself by processing data through trained models and generating labels autonomously, thereby improving productivity and automation.
2Productivity
If machine learning classifier is integrated for automatic classification, then inspection efficiency and automation are improved, but the device complexity increases
Solution Approach 1:
The patent merges the ML classifier directly into the inspection tool's control system, combining multiple functions (data acquisition, processing, and classification) into a single integrated system. This reduces the need for separate external processing systems and manages complexity through functional integration.
Solution Approach 2:
The control system is designed with multi-functionality, serving both as the inspection tool's operational controller and as the host for the ML classification engine. This universal design allows the same hardware platform to perform both traditional inspection control and advanced AI-based classification, avoiding the need for additional dedicated hardware.
3Measurement precision
If multiple sensors are used to collect comprehensive data, then measurement precision and detection accuracy are improved, but the device complexity and data processing requirements increase
Solution Approach 1:
The ML classifier serves as an intermediary that processes and integrates data from multiple sensors. Rather than requiring complex manual processing of multi-sensor data, the ML model acts as a mediator that automatically synthesizes information from various sensor inputs to improve detection accuracy while managing the complexity of handling multiple data sources.
Data Source
AI summary
Systems and methods for determining a classification from image data. In an embodiment, an object-based audio data processing system includes a processor configured to receive, from an inspection camera, image data collected from an area of interest, process the image data through a model to determine a classification for an aspect of the area of interest, the model trained with previously received image data and respective previously received or determined classifications, determine, based on the classification, a label for the aspect, and provide the label and the image data to a user interface