Structure Detection With Predictive Image Filter Tuning
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Solution Overview
Problem
Existing computer vision algorithms (CVAs) for object inspection require manual tuning of parameters, which is time-consuming and resource-intensive, especially when adapting to new objects or types of inspection, and machine learning-based approaches demand extensive labeled training data and skilled personnel.
Innovation Solution
Implement a predictive model to automatically determine image filter parameter values for CVAs, using a machine learning process that reduces the need for manual parameter tuning and labeled training data, enabling faster and more accurate structure detection in inspection image data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual parameter tuning is used for computer vision algorithms, then detection accuracy can be optimized, but inspection time and resource requirements increase significantly
Solution Approach 1:
The system enables self-service by allowing the computer vision algorithm to automatically tune its own parameters through iterative processing. The algorithm processes image data, evaluates detection results, and adjusts parameters autonomously without requiring manual intervention, thus maintaining detection accuracy while reducing inspection time and resource requirements.
2Extent of automation
If machine learning-based approaches are implemented, then automated structure detection can be achieved, but extensive labeled training data and skilled personnel are required
Solution Approach 1:
The system applies partial automation by combining automated parameter tuning with selective manual oversight. Rather than requiring full machine learning training with extensive labeled data, the algorithm performs automated detection for routine cases while allowing for minimal human verification, thus achieving automated structure detection without the complexity of comprehensive training datasets.
3Adaptability or versatility
If computer vision algorithms are adapted to new objects, then detection capability is improved, but parameter tuning becomes more time-consuming and resource-intensive
Solution Approach 1:
The system performs preliminary action by pre-configuring the computer vision algorithm with adaptive parameter tuning capabilities before actual inspection begins. The algorithm is prepared to automatically adjust parameters when encountering new object types, eliminating the need for time-consuming manual re-tuning and maintaining high inspection efficiency across diverse object types.
Data Source
AI summary
A method for detecting structures is provided. The method can include receiving inspection image data characterizing a region of interest of an object being inspected. The regions of interest can include one or more structures of the object. The method can also include determining, using a computer vision algorithm, a structure within the region of interest with respect to photometric properties of pixel data in the inspection image data. The structure can be determined using a predictive model trained to determine image filter parameter values for image filters of the computer vision algorithm based on applying optimization techniques using training image data and annotation data. An indication of the structure can be provided, for example for display or storage in memory. Systems and computer-readable mediums implementing the method are also provided.