1) Deep learning has achieved great success in many computer vision tasks such as image classification, object detection, and segmentation. Convolutional neural networks (CNNs) are often used.
2) The size and quality of training datasets is crucial, as deep learning models require large amounts of labeled data to learn meaningful patterns. Data augmentation and synthesis can help increase data quantity and quality.
3) Semi-supervised and transfer learning techniques can help address the challenge of limited labeled data by making use of unlabeled data as well. Generative adversarial networks (GANs) have also been used for data augmentation.
1) Deep learning has achieved great success in many computer vision tasks such as image classification, object detection, and segmentation. Convolutional neural networks (CNNs) are often used.
2) The size and quality of training datasets is crucial, as deep learning models require large amounts of labeled data to learn meaningful patterns. Data augmentation and synthesis can help increase data quantity and quality.
3) Semi-supervised and transfer learning techniques can help address the challenge of limited labeled data by making use of unlabeled data as well. Generative adversarial networks (GANs) have also been used for data augmentation.
1) The document discusses using deep learning for image-based inspection of defects in manufacturing applications. Deep learning can detect variable, deformable defects better than rule-based tools.
2) Challenges for automatic optical inspection include labelling large images, detecting defects in low contrast materials like silicone rubber gaskets, and irregularly shaped defects.
3) The author's research has developed a system architecture and toolchain for deep learning-based defect detection that includes data capture, preprocessing, model training and inference, and result display. Their work has achieved defect detection in applications such as inner ring thickness measurement and silicone gasket inspection.
This document outlines a methodology for developing an artificial intelligence algorithm to detect head-in-pillow defects in solder ball inspection. It discusses acquiring 3D images of printed circuit boards using x-ray projection to better identify the location of these defects. The goals are to solve data imbalance issues from the rare defective samples and compare performance to other CNN models and commercial inspection software. Key challenges addressed include overfitting from limited data and properly preprocessing x-ray images to reduce noise before 3D reconstruction.
This document discusses trends in industrial IoT and how equipment companies can respond. It outlines Cathy Yeh's agenda which includes trends in industrial IoT and Microsoft's strategy, smart manufacturing use cases, and an innovative business model example using AOI cloud. It describes challenges facing global manufacturing from trends like shortened product lifecycles, rapidly changing operating environments, and industry ecosystem changes. It then provides examples of how industrial IoT solutions from Microsoft and partners can provide insights, reduce costs, and enable new revenue streams for customers through solutions like predictive maintenance and remote monitoring.
This document discusses applications of deep learning for automated optical inspection (AOI). It presents case studies on detecting diamond electroplating cutting lines, golf club scratches, rubber defects, and enclosure surface scratches using deep learning models. The document also provides an overview of deep learning history and architectures commonly used for AOI, such as convolutional neural networks. Challenges for applying deep learning to AOI are noted, including handling small defects, limited defect data, imbalanced data sets, and adapting models to new defect patterns.
This document provides an overview of artificial intelligence and how it relates to machine vision and advanced optical inspection. It begins with brief introductions to AI, machine learning, and deep learning. It then discusses key factors driving the AI boom like increased data, computing power, and algorithms. Examples are given of how deep learning has been applied to tasks like surface defect inspection. The document discusses how AI can help with complex machine vision problems and provides an example framework. It explores synergies between AI, machine vision, robotics, sensors, IoT, and other technologies. Finally, it looks at future trends like increased integration between AI and machine vision applications and the rise of related startups.
1. The document discusses using deep learning techniques for surface defect detection, focusing on strategies for dealing with imbalanced training data.
2. It proposes using generative adversarial networks (GANs) to generate synthetic defect samples in order to address the class imbalance problem. Convolutional neural networks (CNNs) are then used for classification.
3. Autoencoding models like convolutional autoencoders (CAE) and variational autoencoders (VAE) can also be used for unsupervised defect detection based on image reconstruction.
1) Dr. Schenk Worldwide is a global company that has been operating since 1985 and is headquartered in Germany with offices worldwide focusing on film and glass inspection products.
2) The document discusses Dr. Schenk's various product lines for inspecting films, optical media, glass, and solar panels.
3) Dr. Schenk aims to improve quality, yield, and provide advantages for customers through reliable defect detection, critical defect classification, and data-driven process optimization.
