8,846 research outputs found
Deep CNN-Based Automated Optical Inspection for Aerospace Components
ABSTRACT
The defect detection problem is of outmost importance in high-tech industries such as aerospace manufacturing and is widely employed using automated industrial quality control systems. In the aerospace manufacturing industry, composite materials are extensively applied as structural components in civilian and military aircraft. To ensure the quality of the product and high reliability, manual inspection and traditional automatic optical inspection have been employed to identify the defects throughout production and maintenance. These inspection techniques have several limitations such as tedious, time- consuming, inconsistent, subjective, labor intensive, expensive, etc. To make the operation effective and efficient, modern automated optical inspection needs to be preferred. In this dissertation work, automatic defect detection techniques are tested on three levels using a novel aerospace composite materials image dataset (ACMID). First, classical machine learning models, namely, Support Vector Machine and Random Forest, are employed for both datasets. Second, deep CNN-based models, such as improved ResNet50 and MobileNetV2 architectures are trained on ACMID datasets. Third, an efficient defect detection technique that combines the features of deep learning and classical machine learning model is proposed for ACMID dataset. To assess the aerospace composite components, all the models are trained and tested on ACMID datasets with distinct sizes. In addition, this work investigates the scenario when defective and non-defective samples are scarce and imbalanced. To overcome the problems of imbalanced and scarce datasets, oversampling techniques and data augmentation using improved deep convolutional generative adversarial networks (DCGAN) are considered. Furthermore, the proposed models are also validated using one of the benchmark steel surface defects (SSD) dataset
A convolutional autoencoder approach for mining features in cellular electron cryo-tomograms and weakly supervised coarse segmentation
Cellular electron cryo-tomography enables the 3D visualization of cellular
organization in the near-native state and at submolecular resolution. However,
the contents of cellular tomograms are often complex, making it difficult to
automatically isolate different in situ cellular components. In this paper, we
propose a convolutional autoencoder-based unsupervised approach to provide a
coarse grouping of 3D small subvolumes extracted from tomograms. We demonstrate
that the autoencoder can be used for efficient and coarse characterization of
features of macromolecular complexes and surfaces, such as membranes. In
addition, the autoencoder can be used to detect non-cellular features related
to sample preparation and data collection, such as carbon edges from the grid
and tomogram boundaries. The autoencoder is also able to detect patterns that
may indicate spatial interactions between cellular components. Furthermore, we
demonstrate that our autoencoder can be used for weakly supervised semantic
segmentation of cellular components, requiring a very small amount of manual
annotation.Comment: Accepted by Journal of Structural Biolog
GOGGLES: Automatic Image Labeling with Affinity Coding
Generating large labeled training data is becoming the biggest bottleneck in
building and deploying supervised machine learning models. Recently, the data
programming paradigm has been proposed to reduce the human cost in labeling
training data. However, data programming relies on designing labeling functions
which still requires significant domain expertise. Also, it is prohibitively
difficult to write labeling functions for image datasets as it is hard to
express domain knowledge using raw features for images (pixels).
We propose affinity coding, a new domain-agnostic paradigm for automated
training data labeling. The core premise of affinity coding is that the
affinity scores of instance pairs belonging to the same class on average should
be higher than those of pairs belonging to different classes, according to some
affinity functions. We build the GOGGLES system that implements affinity coding
for labeling image datasets by designing a novel set of reusable affinity
functions for images, and propose a novel hierarchical generative model for
class inference using a small development set.
We compare GOGGLES with existing data programming systems on 5 image labeling
tasks from diverse domains. GOGGLES achieves labeling accuracies ranging from a
minimum of 71% to a maximum of 98% without requiring any extensive human
annotation. In terms of end-to-end performance, GOGGLES outperforms the
state-of-the-art data programming system Snuba by 21% and a state-of-the-art
few-shot learning technique by 5%, and is only 7% away from the fully
supervised upper bound.Comment: Published at 2020 ACM SIGMOD International Conference on Management
of Dat
Digital reality: a model-based approach to supervised learning from synthetic data
Hierarchical neural networks with large numbers of layers are the state of the art for most computer vision problems including image classification, multi-object detection and semantic segmentation. While the computational demands of training such deep networks can be addressed using specialized hardware, the availability of training data in sufficient quantity and quality remains a limiting factor. Main reasons are that measurement or manual labelling are prohibitively expensive, ethical considerations can limit generating data, or a phenomenon in questions has been predicted, but not yet observed. In this position paper, we present the Digital Reality concept are a structured approach to generate training data synthetically. The central idea is to simulate measurements based on scenes that are generated by parametric models of the real world. By investigating the parameter space defined of such models, training data can be generated in a controlled way compared to data that was captured from real world situations. We propose the Digital Reality concept and demonstrate its potential in different application domains, including industrial inspection, autonomous driving, smart grid, and microscopy research in material science and engineering
Machine Learning in Manufacturing towards Industry 4.0: From ‘For Now’ to ‘Four-Know’
While attracting increasing research attention in science and technology, Machine Learning (ML) is playing a critical role in the digitalization of manufacturing operations towards Industry 4.0. Recently, ML has been applied in several fields of production engineering to solve a variety of tasks with different levels of complexity and performance. However, in spite of the enormous number of ML use cases, there is no guidance or standard for developing ML solutions from ideation to deployment. This paper aims to address this problem by proposing an ML application roadmap for the manufacturing industry based on the state-of-the-art published research on the topic. First, this paper presents two dimensions for formulating ML tasks, namely, ’Four-Know’ (Know-what, Know-why, Know-when, Know-how) and ’Four-Level’ (Product, Process, Machine, System). These are used to analyze ML development trends in manufacturing. Then, the paper provides an implementation pipeline starting from the very early stages of ML solution development and summarizes the available ML methods, including supervised learning methods, semi-supervised methods, unsupervised methods, and reinforcement methods, along with their typical applications. Finally, the paper discusses the current challenges during ML applications and provides an outline of possible directions for future developments
Automated Defect Detection of Screws in the Manufacturing Industry Using Convolutional Neural Networks
Defect detection in industrial production processes is an important and necessary part of quality control. Many defects can occur during the manufacturing process, causing high manufacturing costs. Thus the inspection of screws, which represent an indispensable element of many mechanical components, is a critical process. To reduce manufacturing costs and increase efficiency, a reliable method for inspection is Deep Learning. It can help simplify the process of quality control and increase the velocity and volume of detected defects in screws. This approach uses a CNN model to classify non-defective and defective screws with different types of defects. Instead of manual quality control methods, which can be easily biased, our CNN approach is accurate, cost-efficient, and fast, with an accuracy of over 97 percent. With this approach corresponding to industrial production processes, different defects in screws and non-defective screws can be classified from images according to a real-world industrial inspection scenario
Data-Mining a Large Digital Sky Survey: From the Challenges to the Scientific Results
The analysis and an efficient scientific exploration of the Digital Palomar
Observatory Sky Survey (DPOSS) represents a major technical challenge. The
input data set consists of 3 Terabytes of pixel information, and contains a few
billion sources. We describe some of the specific scientific problems posed by
the data, including searches for distant quasars and clusters of galaxies, and
the data-mining techniques we are exploring in addressing them.
Machine-assisted discovery methods may become essential for the analysis of
such multi-Terabyte data sets. New and future approaches involve unsupervised
classification and clustering analysis in the Giga-object data space, including
various Bayesian techniques. In addition to the searches for known types of
objects in this data base, these techniques may also offer the possibility of
discovering previously unknown, rare types of astronomical objects.Comment: Invited paper, to appear in Applications of Digital Image Processing
XX, ed. A. Tescher, Proc. S.P.I.E. vol. 3164, in press; 10 pages, a
self-contained TeX file, and 3 separate postscript figure
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