Multi-Sensor Pipe Interior Imaging for Single-Pass Inspection
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Solution Overview
Problem
Multi-sensor inspection data for pipe interiors is difficult to collect, process, and present accurately due to imprecise sensor data, particularly in smaller pipes, leading to reduced accuracy and increased time, complexity, and cost with current methods requiring multiple passes for data collection.
Innovation Solution
A single-pass inspection technique that intelligently selects image processing techniques based on feature identification using multiple sensor data types, such as LIDAR and structured laser light, to improve image quality without the need for repeated passes through the pipe.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple passes are used for data collection, then measurement precision improves, but loss of time increases
Solution Approach 1:
The system performs preliminary classification of pipe features using multiple sensor data types (LIDAR, structured laser light, visible light) during a single pass. This preliminary action enables the selection of appropriate image processing techniques without requiring multiple re-passes, thereby maintaining measurement precision while reducing inspection time.
Solution Approach 2:
The patent combines data from multiple sensor types (LIDAR, structured laser light, visible light cameras) collected during a single pass to create a comprehensive dataset. This merging of multi-sensor data allows for accurate feature identification and image processing technique selection, achieving high measurement precision in one pass rather than requiring multiple separate passes.
2Measurement precision
If multiple sensor data types are collected and processed, then measurement precision improves, but device complexity increases
Solution Approach 1:
The system segments the data processing task by first classifying pipe features using multiple sensor data types, then selecting specific image processing techniques based on the identified feature type. This segmentation of the processing workflow into classification followed by targeted processing reduces overall complexity while maintaining high measurement precision.
Solution Approach 2:
The patent applies different image processing techniques to different pipe features based on their specific characteristics. Instead of applying a uniform complex processing algorithm to all data, the system tailors the processing approach to each feature type (e.g., cracks, deformations, deposits), thereby reducing overall device complexity while maintaining high measurement precision for each specific feature.
3Loss of information
If traditional image processing is used without intelligent selection, then device complexity remains low, but loss of information increases
Solution Approach 1:
The system uses feedback from the classification stage to dynamically select appropriate image processing techniques. The classification results from multiple sensor data types provide feedback that guides the selection of processing methods, ensuring that the most suitable technique is applied to each feature type. This feedback mechanism reduces information loss by matching processing techniques to feature characteristics while managing device complexity through automated selection.
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
This approach enhances image accuracy and reduces the time and cost of pipe inspections by leveraging multiple sensor data types to select appropriate image processing methods for specific pipe features, improving image quality with fewer data collection passes.
Implementation Method 1
a first data type obtained using a first sensor type and a second data type obtained using a second sensor type
Implementation Method 2
structured laser light
Data Source
AI summary
An embodiment provides a method, including: obtaining, from a multi-sensor pipe inspection robot that traverses through the interior of a pipe, two or more sets of condition assessment data for the interior of the pipe collected during a single pass through the interior of the pipe; the two or more sets of condition assessment data comprising a first data type obtained using a first sensor type and a second data type obtained using a second sensor type; combining, using a processor, two or more image processing techniques to adjust imaging of a pipe feature; and forming, using the processor, an image of the interior of the pipe using the two or more image processing techniques. Other embodiments are described and claimed.