Next-Generation Automated Crack Detection powered by Image Segmentation

Based on Patent Research | WO-2022045544-A1 (2022)

Documenting building damage, particularly crack identification, is crucial for structural integrity and effective maintenance. Traditionally, creating crack state drawings involves time-consuming manual inspection and drawing, often leading to human error and inconsistent records. This inefficiency hinders timely repairs. Image segmentation, a computer vision technique that precisely outlines objects within images, offers a solution. It accurately identifies and delineates cracks, enabling the automated generation of detailed crack state drawings. This improves efficiency and ensures consistent, reliable documentation for building management.

SOLUTION OVERVIEW

From Manual Inspection to AI Analysis Technology

Image Segmentation technology directly addresses the challenges of manual damage documentation in building construction. This computer vision technique precisely identifies and delineates cracks within images of building surfaces. The process begins with capturing visual data, which algorithms then analyze to automatically outline each defect. This precise digital mapping enables the automated generation of detailed crack state drawings. It replaces traditional time-consuming visual inspections and hand-drawn records with an objective, digital method.

The practical application of this technology lies in its potential for automation, seamlessly integrating into existing building management workflows. It significantly enhances the consistency and reliability of damage reports, accelerating maintenance and repair decisions. Think of it like a digital blueprint that automatically updates with every hairline fracture, providing an immediate, precise record of a building's structural health. This capability leads to more efficient resource allocation for facility upkeep, ensuring proactive maintenance and optimized operational improvements for building assets.

HOW IT WORKS

Reading building cracks in images

Capturing Building Imagery

The process begins by gathering comprehensive visual data from building surfaces, often using standard imaging equipment. These high-resolution images serve as the initial raw input, providing a detailed visual record of the structure's current condition for subsequent analysis.

Analyzing Visual Information

Advanced AI algorithms then meticulously examine the captured images to detect subtle anomalies and potential damage indicators. This initial scan efficiently identifies specific regions where cracks or other structural imperfections might be present, preparing the data for precise assessment.

Delineating Crack Boundaries

Utilizing sophisticated image segmentation techniques, the system precisely outlines and maps each identified crack at a pixel-accurate level. This crucial step transforms raw detections into accurate, measurable digital representations of every defect on the building surface.

Generating Crack State Drawings

Finally, the precisely delineated crack data is compiled to automatically produce detailed and standardized crack state drawings. These digital drawings provide clear, consistent, and reliable documentation, essential for informing proactive building maintenance and repair decisions.

Potential Benefits

Enhanced Accuracy and Consistency

Image segmentation precisely identifies and delineates cracks, eliminating human error and ensuring uniform documentation. This leads to more reliable structural integrity assessments for buildings.

Accelerated Documentation Process

Automating crack state drawing generation significantly reduces the time spent on manual inspections and record-keeping. This speeds up reporting and allows for quicker decision-making in building maintenance.

Optimize Maintenance Resource Allocation

With precise, up-to-date crack data, facility managers can prioritize repairs and allocate resources more effectively. This ensures proactive maintenance and reduces unnecessary operational costs for building assets.

Reliable Data for Decisions

The system provides objective, consistent digital records of building damage, replacing subjective manual reports. This empowers better, data-driven decisions for structural upkeep and safety.

Implementation

1 Set Up Imaging. Install or prepare cameras, drones, or mobile devices for high-resolution image acquisition of building surfaces.

2 Deploy AI Platform. Install and configure image segmentation software on infrastructure, ensuring connectivity for data processing.

3 Calibrate System. Fine-tune AI model parameters and validate performance with example building imagery specific to the project environment.

4 Perform Inspections. Capture building images using the protocol, then upload to the AI platform for automated crack detection.

5 Generate Documentation. Review automatically generated crack state drawings and integrate these precise digital records into building management systems.

Source: Analysis based on Patent WO-2022045544-A1 "Method for automatically generating crack state drawing of building, and worker terminal having program installed therein to execute method for automatically generating crack state drawing of building" (Filed: March 2022).