Multi-Modal Defect Inspection for Occluded and Internal Flaws
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Solution Overview
Problem
Automated defect inspection systems face challenges such as occlusions, varying lighting conditions, and limited field of view, especially when relying solely on vision-based modalities, leading to inefficiencies and inaccuracies in defect detection.
Innovation Solution
Employing a multi-modal approach that combines RGB cameras for visual inspection with UWB radar for structural imaging, utilizing deep learning techniques to integrate and analyze data from both modalities for enhanced defect classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If vision-based inspection is used, then the system is simple and cost-effective, but defect detection accuracy deteriorates due to occlusions and limited field of view
Solution Approach 1:
The patent combines multiple inspection modalities (vision-based inspection and radar-based inspection) into a unified multi-modal inspection system. The vision system captures surface defects while the radar system penetrates occlusions and detects subsurface defects, creating a complementary inspection approach that overcomes the limitations of each individual modality.
Solution Approach 2:
The patent introduces a new inspection dimension by adding radar-based subsurface inspection to the traditional vision-based surface inspection. This dimensional expansion from surface-only to surface-and-subsurface inspection enables detection of defects that were previously invisible to vision systems, particularly those hidden behind occlusions or within materials.
2Measurement precision
If multi-modal inspection is used, then defect detection accuracy improves, but device complexity increases
Solution Approach 1:
The patent segments the inspection task into distinct modalities (vision and radar) with specialized processing pipelines. Each modality has its own defect detection algorithms optimized for its data characteristics, and the results are subsequently integrated. This segmentation allows each component to be independently optimized and maintained while achieving superior overall performance.
Solution Approach 2:
The patent employs an intermediary integration layer that combines results from vision-based and radar-based inspection systems. This intermediary component reconciles data from different modalities, resolves conflicts between detection results, and produces a unified defect assessment, thereby managing the complexity of multi-modal integration.
3Measurement precision
If high-quality images are captured, then defect detection accuracy improves, but data storage and transmission costs increase
Solution Approach 1:
The patent extracts and utilizes structural information from radar signals that is inherently present in the inspection data. By leveraging this embedded structural information for defect detection, the system avoids the need to store and transmit large volumes of high-resolution image data, thereby reducing data storage and transmission costs while maintaining detection accuracy.
Solution Approach 2:
The patent changes the inspection parameter from capturing high-resolution visual images to acquiring radar signal characteristics that directly reveal subsurface structural information. This parameter change enables defect detection through alternative physical properties (electromagnetic wave interaction with materials) rather than relying on high-resolution optical imaging, thus reducing data volume requirements.
Data Source
AI summary
Systems and methods described herein relate to multi-modal defect inspection. An image of an inspected item and structural imaging data of the inspected item are obtained. The image is captured by at least one optical sensor and the structural imaging data is obtained via at least one structural imaging sensor. The image is processed via a first machine learning model to obtain a first defect classification. At least some of the structural imaging data is processed via a second machine learning model to obtain a second defect classification. An inspection result is automatically generated based on the first defect classification and the second defect classification. The inspection result is caused to be presented at a user device in association with an item identifier of the inspected item.