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. Previously, he led YouTube’s video classification team and was founder and CTO of two successful companies (a telco operator and a strategy firm). Aurélien is the author of several technical books, including the O’Reilly book Hands-on Machine Learning with Scikit-Learn and TensorFlow.
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