Thesis topic proposal
János Botzheim
Visual recognition supported by artificial intelligence in industrial automation


Institute: Eötvös Loránd University, Budapest
computer sciences
Doctoral School of Informatics

Thesis supervisor: János Botzheim
Location of studies (in Hungarian): Helyszín angolul Eötvös Loránd University, Faculty of Informatics
Abbreviation of location of studies: IK

Description of the research topic:

A dynamically developing field of machine learning is computer vision, pattern recognition in an industrial environment. The area has undergone an explosive evolution over the last few years. Sensors have become commonplace and, despite the large amount of the information they extract, real time data processing is feasable. Today, it is possible to perform complex operations with low error rates, reliably, beyond the capabilities of the human workforce.

Typical application examples include the reduction of downtime due to an abnormal product, where the abnormal product is detected by the decision system from the (usually visual) information from the sensor system. It is the responsibility of the AI to recognize the anomaly and handle it in collaboration with the production line to maintain quality and prevent further faults. It has to respond to environmental influences and improve itself and thereby increase the robustness of the method.

This research provides an opportunity for a successful applicant to acquire skills and in-depth expertise in the concepts of artificial intelligence. The candidate learns about the state-of-the-art algorithms in the field and develops new ones, which is used in the subject of the study - to detect the anomalies that occur. The output of the research plan is to investigate and develop machine vision based on artificial intelligence methods used in industrial applications.

Required language skills: English
Recommended language skills (in Hungarian): Hungarian
Further requirements: 
SW: Python

personal skills: Proactive student with good communication skills who is enthusiastic about data. Cooperation ability in industrial and academical environment as well, industry experience (min. 2 years, preferably in development)

Number of students who can be accepted: 1

Deadline for application: 2023-05-31

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