AI-Based Multispectral Imaging for Material Property Classification
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Solution Overview
Problem
Existing methods for inspecting underground pipelines are time-consuming, prone to errors, and require human intervention, making them inefficient for early detection of damage.
Innovation Solution
A method and system utilizing AI-based multi-spectral imaging with radiation sources emitting different wavelength ranges in a temporal sequence, combined with multi-spectral image acquisition, to automatically detect and classify material properties indicative of damage, such as cracks, roots, and surface defects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual inspection methods are used, then personnel can physically access pipelines, but the inspection process becomes time-consuming and error-prone
Solution Approach 1:
The patent replaces manual mechanical inspection with an automated optical measurement system. A probe equipped with multiple sensors (visible light camera, UV sensor, IR sensor) automatically captures images and spectral data of the pipeline interior surface, eliminating the need for human personnel to physically access and visually inspect the pipelines, thereby reducing inspection time while maintaining or improving detection accuracy through automated image processing and spectral analysis
Solution Approach 2:
The inspection system performs self-service through automated data acquisition and analysis. The probe automatically captures multi-spectral images, the system automatically processes and analyzes the spectral data to detect material properties and damages, and the system automatically generates inspection reports, eliminating the need for human intervention in the core inspection process and significantly reducing inspection time
2Measurement precision
If multi-spectral imaging is used, then detection precision improves, but device complexity increases
Solution Approach 1:
The patent merges multiple sensing capabilities into a single integrated probe system. The visible light camera, UV sensor, and IR sensor are combined in one probe that can be introduced into the pipeline, allowing simultaneous multi-spectral data collection. This integration reduces the overall system complexity compared to using separate inspection systems, while maintaining high measurement precision through comprehensive spectral analysis
Solution Approach 2:
The inspection system achieves universality by using a single multi-spectral probe to detect various material properties and damage types. The same probe configuration can identify different material compositions, structural defects, and contamination types through multi-spectral imaging, eliminating the need for multiple specialized inspection devices and reducing 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
Enables early and accurate detection of pipeline damage, reducing the need for manual inspection and minimizing maintenance costs by automating the classification and recognition of complex signals.
Implementation Method 1
at least one radiation source configured to emit radiation of different wavelength ranges in a temporal sequence
Implementation Method 2
capturing, by one or more multi-spectral image acquisition devices, radiation reflected by the object
Data Source
AI summary
For determining one or more material properties of an object (10), radiation of different wavelength ranges from at least one radiation source (2) is projected onto the object (10), in a temporal sequence, one wavelength at a time. The different wavelength ranges of radiation projected onto the object (10) and the temporal sequence are pre-set by an Artificial Intelligence (AI) model engine (8) provided by a data acquisition and evaluation system (7). Radiation reflected by the object (10) is captured by one or more multi-spectral image acquisition devices (12), and multiple images, and corresponding image data, are generated according to the reflected radiation of multiple wavelengths respectively. A set of raw data is formed to output to the data acquisition and evaluation system (7) for data analysis to determine one or more material properties of the object (10), wherein the set of raw data comprises the image data.