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Computer Science/Machine Learning/Computer Vision
Scope
Two students completing Master Thesis 30 hp each
Background
Manual assembly in production comes with mistakes such as forgetting to assemble parts (e.g. screws gaskets cables). A camera together with automatic optical inspection (AOI) can be used to detect such errors before the unit is fully assembled. Traditional AI research for industrial anomaly detection (IAD) typically relies on discriminative models within the conventional deep learning paradigm. It requires retraining the model when production lines or requirements change. Another alternative solution is to use generative models and large language models to perform industrial quality inspections based on input language and visual prompts such as AnomalyGPT.
Goal
The goal is to implement AOI as a proof of concept with the highest quality possible. When implementing the proof of concept also consider its potential future applications and improvements. What is a feasible machine learning model to use together with a selected data analysis technique (e.g. anomaly detection) and how can an ecosystem be established to train it The performance of various model implementations across different tasks will be compared and studied.
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Who to contact for any questions regarding the position!
Thomas Bezerra
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