Temporal Computer Vision for More Accurate NDT Inspection
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Solution Overview
Problem
Traditional computer vision algorithms for non-destructive testing (NDT) are limited by the accuracy of predictions made from single images, leading to non-optimized results due to insufficient information utilization.
Innovation Solution
An inspection system employing a camera and sensors to capture images over time, utilizing a first computer vision algorithm for initial predictions and a second algorithm that incorporates motion data and previous image predictions to enhance accuracy, including the use of convolutional neural networks (CNNs) and recurrent neural networks (RNNs) like Long Short Term Memory (LSTM) networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional computer vision algorithms are used for single image analysis, then the system complexity remains low, but the prediction accuracy is limited
Solution Approach 1:
The patent transitions from analyzing single static images to analyzing sequences of images across the time dimension. By incorporating temporal information from multiple frames and motion data from sensors, the system creates a multi-dimensional analysis approach that significantly improves prediction accuracy while managing complexity through structured processing pipelines
Solution Approach 2:
The system performs preliminary actions by capturing and preprocessing image sequences and motion data before the actual prediction analysis. Motion parameters are calculated in advance from sensor data, and image sequences are buffered and pre-processed, allowing the main prediction algorithm to work with prepared data rather than raw inputs, thus improving accuracy without proportionally increasing complexity
2Measurement precision
If multiple images and motion data are integrated for analysis, then prediction accuracy improves, but the processing complexity increases
Solution Approach 1:
The patent segments the complex analysis task into distinct modular components: image acquisition, motion data acquisition, motion parameter calculation, image sequence processing, and integrated prediction. Each module handles a specific aspect of the data, making the overall complex processing manageable through clear separation of concerns and specialized processing for each data type
Solution Approach 2:
Motion parameters serve as intermediaries that bridge the gap between raw sensor data and the final prediction analysis. The system calculates motion parameters (such as velocity, acceleration, orientation) from raw motion data and uses these derived parameters in the prediction algorithm, creating an intermediate representation that simplifies the integration of multiple data sources
Data Source
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AI summary
Systems and methods for target inspection are provided. The system includes a camera, at least one sensor, and a controller. The camera acquires images of a target over time and the sensor(s) acquire motion data characterizing camera and target movement. The controller generates, using a first computer vision (CV) algorithm, an initial prediction and confidence level regarding an object of interest for a first image acquired at a first time. The controller also determines, using the motion data, a motion parameter characterizing relative motion between the camera and the target at the first time. The controller additionally receives a weighting based upon a second image acquired at a second time prior to the first time. The controller generates, using a second CV algorithm, a final prediction and confidence level for the first image based upon the first image, the initial prediction and confidence level, the motion parameter, and the weighting.