AI Driven Image Viewer Application for Defect Detection in Buildings using Drone Images

Client

The client is a globally recognized engineering consulting firm that excels in structural engineering, sustainability, protective design, and advanced analytics, offering innovative solutions through cutting-edge technology, including AI and digital twins, to create safe, efficient, and sustainable projects across diverse industries.

Need

The objective was to develop a deep learning-based image viewer application that can be used by organisations and structural project owners to analyse, monitor, and maintain damage detection of structures using drone-captured images.

Solutions

Damage Detection Architecture

Developed deep learning architecture for detecting damages and defects on drone-captured images, Orthomosaics, and 2D/3D images.

Lifecycle Management UI

Developed an interactive UI to maintain the lifecycle of detections for every project.

Custom DenseNet Model

Developed a custom DL model using DenseNet to achieve better results.

Training & Prediction Framework

Built a comprehensive framework for training and prediction, enabling the model to be easily retrained for new event types as needed, utilizing Supervise.ly and Kubernetes/Kubeflow for ML training and inference pipelines.

Benefits

  • Image viewer application handles various types (2D, 3D, Panorama images, Video, etc.) and sizes of images taken from drones in one platform.
  • AI-driven defect detection and annotation.
  • Correction of annotated defect images in the application.
  • Dashboard for the user to see all the project details.

Key Services

  • AI Driven Defect Detection
  • Maintenance Application on Cloud

Tools and Technologies

  • Supervise.ly
  • Kubernetes/Kubeflow
  • Node.js
  • Angular
  • three.js
  • Azure services

Client

The client is a globally recognized engineering consulting firm that excels in structural engineering, sustainability, protective design, and advanced analytics, offering innovative solutions through cutting- edge technology, including AI and digital twins, to create safe, efficient, and sustainable projects across diverse industries

Need

The objective was to develop a deep learning-based image viewer application that can be used by organisations and structural project owners to analyse, monitor, and maintain damage detection of structures using drone-captured images.

Solutions

Damage Detection Architecture

Developed deep learning architecture for detecting damages and defects on drone-captured images, Orthomosaics, and 2D/3D images.

Training & Prediction Framework

Built a comprehensive framework for training and prediction, enabling the model to be easily retrained for new event types as needed, utilizing Supervise.ly and Kubernetes/Kubeflow for ML training and inference pipelines.

Lifecycle Management UI

Developed an interactive UI to maintain the lifecycle of detections for every project.

Custom DenseNet Model

Developed a custom DL model using DenseNet to achieve better results.

Benefits

  • Image viewer application handles various types (2D, 3D, Panorama images, Video, etc.) and sizes of images taken from drones in one platform.
  • AI-driven defect detection and annotation.
  • Correction of annotated defect images in the application.
  • Dashboard for the user to see all the project details.

Key Services

  • AI Driven Defect Detection
  • Maintenance Application on Cloud

Tools and Technologies 

  • Supervise.ly
  • Kubernetes/Kubeflow
  • Node.js
  • Angular
  • three.js
  • Azure services