Pipe Interior Imaging With Feature-Based Multi-Sensor Processing
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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 with sensors like LIDAR, which can have significant noise and errors, leading to reduced accuracy in imaging pipe features like cracks and erosion, especially in smaller pipes.
Innovation Solution
A single-pass inspection technique that intelligently selects image processing techniques based on identified pipe features using multiple sensor data types, such as structured laser light and LIDAR, to improve image quality without the need for multiple data collection passes, leveraging the strengths of different statistical methods for specific features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple sensor types are used to collect pipe inspection data, then the understanding of pipe condition is improved, but the complexity of data processing increases
Solution Approach 1:
The patent segments the multi-sensor data processing into distinct modules: data acquisition from multiple sensors (LIDAR, cameras, acoustic sensors), feature extraction algorithms, and separate analysis pathways for different sensor types. This modular segmentation reduces overall processing complexity while maintaining comprehensive pipe condition assessment
Solution Approach 2:
The patent introduces intermediate processing layers that translate raw multi-sensor data into standardized feature representations. These intermediaries (feature extraction algorithms and data fusion modules) mediate between diverse sensor inputs and final analysis, simplifying the integration of multiple data types
2Measurement precision
If traditional image processing techniques are used on multi-sensor data, then processing is simpler, but image accuracy is reduced due to sensor noise and errors
Solution Approach 1:
The patent applies different image processing techniques to different regions and features within the pipe inspection data. For example, specific algorithms are applied to crack detection zones while other techniques handle general surface inspection, optimizing accuracy for each local feature type while managing overall processing complexity
Solution Approach 2:
The patent dynamically adjusts processing parameters based on identified pipe features. When cracks or erosion are detected, the system changes processing parameters (such as filtering thresholds, resolution levels, or algorithm selection) to enhance accuracy for those specific features while maintaining efficiency
3Measurement precision
If multiple data collection passes are performed to improve image quality, then image accuracy is improved, but inspection time and cost increase
Solution Approach 1:
The patent performs preliminary processing of multi-sensor data during the single inspection pass, applying noise reduction algorithms and feature enhancement techniques in real-time. This preliminary action prepares the data for accurate analysis without requiring additional inspection passes, saving time while maintaining image quality
Solution Approach 2:
The patent replaces the mechanical approach of performing multiple physical inspection passes with computational methods. Advanced image processing algorithms and data fusion techniques substitute for repeated mechanical data collection, achieving improved image quality through software-based enhancement rather than additional hardware passes
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, complexity, and cost of pipe inspections by improving image processing in real-time using feature-level selection of image processing methods, effectively addressing the imprecision issues with existing multi-sensor data collection methods.
Implementation Method 1
structured laser light sensor data
Implementation Method 2
Light Detection and Ranging (LIDAR) sensor data
Implementation Method 3
Light Detection and Ranging (LIDAR) sensor data
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, sensor data, such as structured laser light sensor data and Light Detection and Ranging (LIDAR) sensor data, for the interior of the pipe; identifying a pipe feature using one or more of the sensor data types; selecting an image processing technique based on the pipe feature identified using a stored association between the pipe feature and an image processing technique; and forming an image of the interior of the pipe by implementing the selected image processing technique. Other embodiments are described and claimed.