Wall Diagnostic AI Dataset Generation With Position-Linked Sensors
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Solution Overview
Problem
Existing diagnostic devices for walls lack an effective method for generating training data sets for artificial intelligence, which is crucial for accurate object detection and classification in walls.
Innovation Solution
A method involving recording sensor data from a measuring device, determining its position relative to the wall, labeling the data with ground truth information, and integrating position information to create a training data set for AI training, utilizing camera sensors and position markers like ArUco or ChArUco markers for precise positioning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If position information is integrated into sensor data for AI training, then object detection accuracy is improved, but data processing complexity increases
Solution Approach 1:
The patent merges sensor data with position information from camera sensors and position markers into a unified training dataset. This integration allows the AI system to correlate sensor readings with precise spatial locations, thereby improving object detection accuracy while managing processing complexity through systematic data fusion procedures.
Solution Approach 2:
Position markers (such as ArUco or ChArUco markers) serve as intermediaries between the physical wall structure and the digital training data. These markers provide a reference framework that simplifies the integration of position information into sensor data, enabling accurate spatial correlation without directly complex processing of raw sensor signals.
2Reliability
If ground truth information is used for labeling sensor data, then AI training quality is improved, but time and resources for data preparation increase
Solution Approach 1:
The patent implements preliminary action by pre-defining ground truth information about objects in walls before data collection. Position markers are pre-placed on walls, and object locations are predetermined. This preparation enables automated labeling during data collection, significantly reducing the time and resources required for post-processing while maintaining high AI training quality.
3Measurement precision
If multiple sensors and position markers are integrated, then diagnostic accuracy is improved, but device complexity increases
Solution Approach 1:
The patent applies universality by designing a integrated system where camera sensors serve dual purposes: capturing images for position determination and providing visual data for wall analysis. Position markers serve multiple functions including spatial reference, data synchronization, and calibration. This multi-functionality reduces the need for separate dedicated components, managing system complexity while maintaining high diagnostic accuracy.
Data Source
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AI summary
The invention relates to a computer-implemented method (300) for generating a training data set (143) for training an artificial intelligence (125) for operating a measuring device (100), in particular a wall diagnostic device, comprising: recording (301) sensor data (103) from at least one sensor unit (101) of a measuring device (100); performing (303) a position determination of the measuring device (100) relative to the wall (105) and generating position-related sensor data (103) by a position determination system (145); labeling (305) the position-related sensor data (103) taking ground truth information into account and generating labeled sensor data (103); and summarizing (307) the labeled sensor data (103) to form a training data set (143). The invention further relates to a method (400) for training an artificial intelligence (125).