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World Congress 2023 • Oct 6, 2023

How computers learn to see – Applying AI to industry

Antonia Hahn

Ditch brittle manual feature engineering. Discover how transfer learning and cloud-based CNNs shrink computer vision training requirements to just 1,000 images for scalable industrial anomaly detection.

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#1 about 2 min

Automating quality control in car seat manufacturing

Automating visual inspection of assembly differences resolves significant efficiency challenges for human operators.

#2 about 3 min

Advantages of AI over classical computer vision

Automated AI algorithms bypass complex feature engineering to reliably detect manufacturing anomalies.

#3 about 5 min

Preparing data and labeling for supervised learning

Collecting, labeling, and slicing image data sets the foundation for a robust predictive model.

#4 about 2 min

Understanding layers in a convolutional neural network

Convolutional and pooling layers automatically downscale image data to recognize structural object features.

#5 about 2 min

Utilizing pre-trained models from service providers

Utilizing pre-built algorithms from cloud vendors significantly reduces the required volume of training data.

#6 about 3 min

Evaluating model performance using confusion matrices

Confusion matrices track false negatives to calculate and minimize the rate of undetected physical defects.

#7 about 3 min

Iterating the model to optimize visual inspection results

Adjusting physical lighting conditions and fine-tuning parameters via validation sets fixes model training inaccuracies.

#8 about 3 min

Architecture for deploying AI inspection on the assembly line

Networked cameras feed a specialized classifier which triggers automated robotic sorting based on probability thresholds.

#9 about 3 min

Lessons learned and strategies for image data collection

Authentic factory floor data is critical because generic internet imagery fails to train robust manufacturing models.

Matching moments

2:35 min

Deploying AI-driven foreign object detection in manufacturing environments

Dean Oren Dean Oren +1 · World Congress 2025

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Introduction to safety-critical machine learning in automotive contexts

Jan Zawadzki · World Congress 2022

3:23 min

Integrating artificial intelligence across the automotive production lifecycle

Katrin Lehmann Katrin Lehmann +1 · World Congress 2025

2:25 min

Navigating automotive complexity with AI runtime environments

Daniel Graff +1 · World Congress 2021

2:07 min

Applying hybrid AI to knowledge graphs and safety controls

Jan Schweiger · World Congress 2022

1:04 min

Connecting legacy manufacturing environments to modern AI systems

Freya Menzel Freya Menzel +1 · Europe 2026 Virtual

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