Image and point cloud evaluation

3D point cloud (left) and 2D color image (right) of a scene

Image and point cloud data represent the environment in the form of 2- and 3-dimensional data, respectively. For example, color cameras produce 2D visual (visible to humans) color images, thermal imaging cameras capture thermal signatures in the environment, and 3D LiDAR sensors use laser beams to scan their surroundings in the form of 3D point clouds.

It is often very easy for a human to extract higher-level information from this rich imagery and point cloud data to generate added value. However, this is very time consuming, costly and requires expert knowledge. We support the automation of this task to save costs and valuable staff time in the medium term, as well as to enable innovative applications. For example, we use 2D color images to automatically assess the quality of a workpiece in production, reveal Energy potential in plants using thermal images, and detect DSGVO-compliant pedestrians in 3D point clouds for optimized traffic light control in our cities of tomorrow.

The focus of our services is on the two application areas Smart Factoryand Smart City.

Our service offer:

  • Potential and feasibility analyses for the integration of systems for the automatic evaluation of image or point cloud data in your company or city
  • Conceptual design of systems for image or point cloud evaluation
  • Implementation of the algorithms on different hardware platforms, e.g. embedded hardware for edge computing
  • Training of your employees on the topic of "AI-based optical inspection".
  • Participation as an application-oriented research partner in research and development projects in the consortium with focus on image and point cloud analysis

Core benefits of exemplary applications:

  • Smart Factory: Automation of quality inspections in production
    • Increase in product quality
    • Objectivity in testing
    • Reduction of refunds
    • Support and relief of employees
Optical quality inspection
  • Smart City: Real-time capable monitoring of vehicles in road traffic
    • Vehicle counts to determine the volume of traffic
    • True-to-lane traffic flow detection as the basis for intelligent traffic signal systems
    • Determination of the occupancy of parking spaces
Real time vehicle counting

References / Publications:


  • Sprute, Dennis; Westerhold, Tim; Hufen, Florian; Flatt, Holger; Gellert; Florian: DSGVO-konforme Personendetektion in 3D-LiDAR-Daten mittels Deep Learning Verfahren. In: Bildverarbeitung in der Automation (BVAu), Nov 2022
  • Gutknecht-Stöhr, M.; Friesen, A.; Flatt, H.; Habeck, T.; Großehagenbrock, J.: Automatisierte Qualitätskontrolle: Kartoffeln, KI und Roboter. Atp magazin 10/2019, S. 72 ff, 2019
 

Project: KI4PED

 

Project: KI4LSA

 

Project: Evaluation of laser- and image-based quality inspection methods

 

Project: Evaluation of self-learning optical methods for quality control

 

Project: Pedestrian frequency measurement in real time

 

Project: UAV-SRGK