Addressing foundation and wall cracks is crucial in construction, as weakened structural integrity can lead to significant damage or even a building collapse in the future. Early detection and management of such cracks are crucial in reducing such accidents.
Computer Vision, with the use of various sensors and cameras, significantly improves the detection of cracks in walls and foundations and at the same time makes the overall process efficient and reliable.
Technical Viewpoints
Using computer vision for wall or foundation crack detection involves a series of steps:
- Cameras and Sensors for Image and Video Capturing: High-resolution images and videos are captured using cameras. These can be either smartphones, dedicated cameras, or using drones installed with cameras. Additionally, infrared and thermal cameras are used that help detect cracks invisible to the human eye.
- Processing Videos and Images: Preprocessing techniques like histogram equalization, and contrast adjustments are applied to make cracks more visible and reduce any lightning and texture variations.
- Object Detection and Segmentation Models: Computer Vision models such as Mask R-CNN and YOLO are used to segment the image, and localize cracks in images. These ML models extract features from images that are difficult for humans to analyze and process. After training is complete, these models can detect and classify images with cracks into severity levels (such as minor, moderate, and major cracks)
- Analysis and Reporting: CV systems generate report data that consists of current crack images with the dimensions, and location. Additionally, these systems also provide a trend and severity of cracks compared with previous records.
- Long-term Monitoring and Maintenance Prediction: The automated CV system analyzes cracks continuously and detects changes. If the severity of cracks gets to dangerous levels, the system can notify an engineering team. Additionally, these systems also forecast the potential structure issues based on observed cracks, helping with taking appropriate measures and reducing the risk of sudden building collapse.
Use cases
- Building and Infrastructure Inspection: Routine maintenance and inspections of constructed buildings or ongoing projects to identify cracks and adopt early maintenance plans.
- Construction Quality Control: Engineers can inspect newly constructed buildings and infrastructure to meet high-quality standards and are free from any defects, even those invisible to the human eye.
- Historical Preservation: Detect and monitor historical buildings and monuments for cracks, and also forecast the cracks and how they could impact the integrity of the building.
- Roads and Dam Projects: Inspect roads, dams, and reservoirs in an automated way for cracks to prevent potential failures.
- Industrial Facilities: Inspect industrial facilities like storage tanks, pipelines, and production plants, to detect cracks and prevent accidents or leaks.
- Remote and Hazardous Environment Monitoring: Structures such as oil rigs and nuclear facilities are hazardous, and manual inspecting is risky and challenging. CV systems coupled with drones can make it possible to inspect these structures for any cracks without exposing human lives to dangerous chemicals or radiation.
Business Value Proposition
The use of computer vision for detecting cracks in walls and foundations offers several business benefits:
- Cost Reduction: Computer Vision systems reduce inspecting costs as they help reduce the manual labor required, especially in routine checks and maintenance. Additionally, early and accurate detection of cracks helps with timely precautions that prevent the structures from further damaging, and reduce repair costs.
- Increased Speed: CV systems provide a faster and more efficient way to manage routine checks for cracks, as manual methods of inspection require a longer time for completion. Additionally, CV systems monitor structures continuously and aid with faster response time.
- Improved Accuracy: Automated systems provide faster inspection with comprehensive analysis records that reduce subjective assessment and decision-making by humans. Also, ML algorithms can detect even small cracks that might be invisible to the human eye, using infrared and heatmap sensors.
- Increased Safety: Remote areas make it challenging for human teams to inspect. CV systems enable teams to inspect such places safely. These help with reducing any accident, and thus reducing costs of damages.
