Image-based quality control “Damage Detection”

Automated inspection of damage and defects using AI-assisted quality control of reflective surfaces.

Where does the AI application offer the greatest benefit?

The inspection of reflective surfaces, for example of quality defects in production or damage to vehicles, is often still carried out manually and therefore turns out to be time-consuming and requires a high level of expertise on the part of the persons in charge. In order to increase the productivity of the processes, “Damage Detection”, an AI system for the quality control of shiny or diffusely reflecting surfaces, has been developed. It is suitable for a wide range of applications: from industrial production, for example in the automotive industry, to the leasing and insurance industry and to automotive appraisals. The system works automatically and takes less than a minute for surface inspection. The quality defects found are categorized using Deep Learning. The combination of deflectometry, i.e. the contact-free detection of reflective surfaces, conventional image recognition processes and artificial intelligence methods is a unique system.

What are the quality indicators of such AI applications?

“Damage Detection” offers low hardware and maintenance costs as a solution. The AI application can be used flexibly and retrofitted into existing productions. Among other things, the solution is able to work under the influence of stray light (e.g. ceiling lighting in a production hall).
The system delivers 100% test coverage. It combines high accuracy with low hardware requirements. Currently, defects as small as 0.1 mm can be detected on 1 m component size.
By using Artificial Intelligence, the system can be trained to detect various types of defects on the surface.
In addition to highly reflective surfaces, the latest developments also allow diffusely reflective surfaces to be tested for their quality.

“The combination of conventional image recognition
methods, AI methods and the detection of reflective
surfaces is unique.”

Dr. rer. nat. Theresa Bick

Fraunhofer Institute for Intelligent Analysis and Information Systems IAIS

What AI technology powers the demonstrator of KI.NRW?

Computer Vision

The solution relies on Convolutional Neural Networks (CNNs) for detection and classification of the surface anomalies. The CNN model belongs to the Artificial Neural Networks, which are suitable for image recognition and understanding.

Informed Machine Learning

By incorporating expert knowledge, the system is able to make dataintensive neural networks practical for industrial production. The amount of training data and the annotation effort can be kept comparatively low.

Combination with classical image processing

The AI is linked to classical image processing. The system uses the advantages of both worlds – fast, approximate algorithms from classical image processing and the powerful methods of deep learning.

What does the AI demonstrator show?

Quality control of shiny or diffusely reflecting surfaces is notable for its simple, mobile hardware design. It operates independently of ambient light and is fully automated. It takes less than one minute for a surface inspection.