Thermal Asset Imaging With Perspective Correction and AI Detection
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Solution Overview
Problem
Imaging industrial assets is a time-consuming and error-prone process, especially in dynamic environments with varying imaging conditions, making it difficult to accurately identify and analyze asset characteristics and operational states.
Innovation Solution
Utilizing neural networks and computer vision algorithms for automated image processing, combined with affine and homographic transformations, to normalize imaging variations and accurately identify assets and their operational attributes, enabling autonomous or semi-autonomous image capture and temperature measurement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual imaging and review of industrial assets is performed, then users can identify assets and detect anomalies, but the process becomes time-consuming and error-prone
Solution Approach 1:
The system enables automated asset identification and anomaly detection through computer vision algorithms that process thermal and visual images without human intervention. The neural network models automatically detect assets, extract features, and identify anomalies, allowing the system to serve itself rather than requiring manual review by users.
Solution Approach 2:
The patent replaces manual mechanical inspection processes with automated computer vision and machine learning systems. Instead of users physically examining assets and manually analyzing images, the system uses automated image processing, neural networks, and algorithms to perform detection and analysis, substituting human effort with computational processes.
2Adaptability or versatility
If images are captured from various locations and angles in dynamic environments, then comprehensive asset coverage is achieved, but imaging variations complicate accurate analysis
Solution Approach 1:
The system dynamically adapts to varying imaging conditions by using neural networks that can process images captured from different angles, distances, and environmental conditions. The model learns to recognize assets and features despite changes in perspective, lighting, and background, enabling accurate identification across diverse capture scenarios.
Solution Approach 2:
The patent employs image processing techniques that adjust and normalize various image parameters including brightness, contrast, color balance, and geometric transformations. By automatically correcting for variations in imaging conditions and standardizing image characteristics, the system maintains measurement precision across different capture scenarios.
3Loss of information
If thermal imaging is used to identify asset temperature and operational state, then additional operational information is obtained, but the complexity of analyzing thermal data increases
Solution Approach 1:
The system segments thermal images into distinct regions corresponding to different assets, components, or areas of interest. By dividing the image into manageable segments and analyzing each separately, the system extracts thermal information efficiently without being overwhelmed by the complexity of the entire image, reducing analysis complexity while preserving information.
Solution Approach 2:
The patent introduces intermediate processing steps including image enhancement, feature extraction, and preprocessing algorithms that bridge the gap between raw thermal data and actionable insights. These intermediary processes simplify the thermal data by extracting relevant features and transforming them into standardized formats that are easier to analyze and interpret.
4Productivity
If automated image processing using neural networks is implemented, then processing speed and accuracy improve, but system complexity increases
Solution Approach 1:
The system performs preliminary actions by pre-processing images before they reach the neural network, including enhancement, normalization, and feature extraction. By preparing images in advance with standardized formats and extracted features, the neural network receives pre-processed data that requires less complex processing, thereby improving speed while managing overall system complexity.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Facilitates rapid and accurate identification and analysis of industrial assets, reducing human error and improving maintenance scheduling through automated asset recognition and temperature monitoring.
Implementation Method 1
a thermal image capture device positioned on a transport instrument that supports autonomous capture of a thermal image of the environment
Data Source
AI summary
Imaging industrial assets to ensure their correct operation or identifying faults can be a time-consuming and error-prone task. By automating the imaging capture process and utilizing neural networks to identify the assets, assets may be quickly and accurately identified as well as their current operational state. Asset imaging may be accomplished via visual or infrared imaging or a combination thereof to identify an asset and to determine if the asset is operating within expected parameters or, if not, to initiate corrective action.