Drone-based Crack Detection

In the Civil Structure (Civil Engineering) concrete surface crack can be considered as a major defect. In order to detect this, a drone building inspection is needed to be done to evaluate the rigidity and tensile strength of the building. In this Crack Detection ML Model, crack detection plays an important role in the building inspection to find those cracks and determining the building health.

Unmanned aerial vehicle (UAV) technologies or also known as drones combined with digital image processing have been applied to the crack inspection of building structures to overcome the drawbacks of manual visual inspection. Surface cracks are currently assessed via manual inspection, which is completely reliant on the competence and experience of qualified employees. It takes a long time to manually examine for surface fractures. As a result of these flaws, crack detection leveraging image processing with deep learning to detect cracks accordingly.

Deploying Machine Learning Model for Drone Edge Computing

Fortunately, UAVs, such as drones, are being used to take photographs in instances where accessibility is an issue, but a human would still have to spend hours and hours reviewing each and every shot taken for signs of damage.

This is where our initiative to modernize the inspection process comes into play. Artificial Intelligence, especially Deep Learning, takes the lead by training our models to be able to replace humans in the time-consuming process of identifying fractures in photographs of buildings.

In this case, the RDA Data Science Team trains a machine learning model for surface crack detection as a good example of a real-life ML pipeline for edge computing. Image stabilization, object identification, and tracking are all components of the computer vision pipeline that RDA wants to bring along.

Our Offerings

RDA’s Data Science Team roll out Computer Vision services and a robust deep learning workflow for image processing:

 

  • We provide easily labeling of images if needed and make the data ready to use for you to train your machine learning model.
  • We ensure deep learning models are easily trained and retrain them automatically when more images become available.
  • We ensure the data quality and the model performance before sending it to the edge computing devices such as drones, camera surveillance, et.