Polarization Imaging for Building Tape Misapplication Detection
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Solution Overview
Problem
Manual inspection and remediation of building defects, particularly in building envelopes, are becoming increasingly difficult due to the size and complexity of modern buildings, leading to reduced precision and safety concerns.
Innovation Solution
Utilizing drones equipped with image capture hardware and machine-learning models to analyze building images for defects, and perform marking or remediation operations, including defect detection using polarization imaging and specialized computing capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual inspection and remediation methods are used, then human workers can perform defect detection and repair, but safety risks increase and human error occurs especially in hard-to-reach areas
Solution Approach 1:
The system enables automated defect detection and remediation where the drone autonomously inspects building surfaces, detects defects using polarization cameras and machine learning models, marks them with dispensing subsystems, and applies remediation materials without continuous human intervention, allowing the system to serve itself in hazardous environments
Solution Approach 2:
The patent replaces manual mechanical inspection and remediation operations with an automated drone system equipped with polarization cameras, machine learning algorithms, and automated dispensing subsystems that can safely access hard-to-reach areas without exposing human workers to risks
2Ease of operation
If drones are used for inspection and remediation, then safety improves and access to hard-to-reach areas is enabled, but device complexity increases
Solution Approach 1:
The drone system is designed as a multi-functional platform that integrates polarization camera hardware for imaging, machine learning processing for defect detection, marking subsystems for identification, and remediation dispensing capabilities, allowing a single device to perform multiple functions that would otherwise require separate systems
Solution Approach 2:
The patent combines previously separate functions (inspection, detection, marking, and remediation) into a single integrated drone system, merging the camera subsystem, processing unit, marking dispensing subsystem, and remediation dispensing subsystem into one cohesive platform that operates autonomously
3Measurement precision
If polarization cameras and machine learning models are used, then measurement precision and data accuracy improve, but processing requirements and computational complexity increase
Solution Approach 1:
The system performs preliminary actions by capturing polarization images that encode surface orientation and defect information, then uses pre-trained machine learning models to process these images, marking defects before remediation occurs, thereby preparing the data and identification in advance to reduce real-time processing demands during critical operations
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
Enhances precision and completeness of defect detection and remediation by reducing human error and improving safety, especially in hard-to-reach areas, through drone maneuverability and advanced image analysis.
Implementation Method 1
The polarization camera hardware is configured to capture a polarization image of a tape as applied to a substrate
Data Source
AI summary
Systems of this disclosure enable building inspection using polarization images. The systems use light polarization information of the polarization images to detect misapplications of tape applied to a substrate. An example system includes polarization camera hardware, a memory communicatively coupled to the polarization camera hardware, and processing circuitry communicatively coupled to the memory. The polarization camera hardware is configured to capture a polarization image of a tape as applied to a substrate. The memory is configured to store the polarization image. The processing circuitry is configured to analyze the polarization image according to a trained classification model and, based on the analysis of the polarization image according to the trained classification model, detect a misapplication with respect to the tape as applied to the substrate.