Computer vision in manufacturing has actually been around for decades: it’s present in thousands of production lines, performing product classification, detecting defective items, gathering data for analytics, and more. Very recently, companies have started to shift from classical computer vision techniques to modern techniques based on deep learning, namely convolutional neural networks (CNNs), which can achieve amazing precision, often reaching or even exceeding human abilities.
Aurélien Géron details common CNN architectures for classification (e.g., ResNet), image segmentation (e.g., DeepLab), object detection (e.g., YOLO), and anomaly detection (e.g., ResNet+SVM), explains how they can be applied to manufacturing, and covers potential challenges along the way, including:
Aurélien Géron is a machine learning consultant at Kiwisoft and author of the best-selling O’Reilly book Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow. Previously, he led YouTube’s video classification team, was a founder and CTO of Wifirst, and was a consultant in a variety of domains: finance (JPMorgan and Société Générale), defense (Canada’s DOD), and healthcare (blood transfusion). He also published a few technical books (on C++, WiFi, and internet architectures), and he’s a lecturer at the Dauphine University in Paris. He lives in Singapore with his wife and three children.
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