A Fast Learning Algorithm for Image Segmentation with Max-Pooling Convolutional Networks

We present a fast algorithm for training MaxPooling Convolutional Networks to segment images. This type of network yields record-breaking performance in a variety of tasks, but is normally trained on a computationally expensive patch-by-patch basis. Our new method processes each training image in a single pass, which is vastly more efficient. We validate the approach in different scenarios and report a 1500-fold speed-up. In an application to automated steel defect detection and segmentation, we obtain excellent performance with short training times

Deep Learning: Our Miraculous Year 1990-1991

In 2020, we will celebrate that many of the basic ideas behind the deep learning revolution were published three decades ago within fewer than 12 months in our "Annus Mirabilis" or "Miraculous Year" 1990-1991 at TU Munich. Back then, few people were interested, but a quarter century later, neural networks based on these ideas were on over 3 billion devices such as smartphones, and used many billions of times per day, consuming a significant fraction of the world's compute.Comment: 37 pages, 188 references, based on work of 4 Oct 201

Automated Quantitative Analyses of Fatigue-Induced Surface Damage by Deep Learning

The digitization of materials is the prerequisite for accelerating product development. However, technologically, this is only beneficial when reliability is maintained. This requires comprehension of the microstructure-driven fatigue damage mechanisms across scales. A substantial fraction of the lifetime for high performance materials is attributed to surface damage accumulation at the microstructural scale (e.g., extrusions and micro crack formation). Although, its modeling is impeded by a lack of comprehensive understanding of the related mechanisms. This makes statistical validation at the same scale by micromechanical experimentation a fundamental requirement. Hence, a large quantity of processed experimental data, which can only be acquired by automated experiments and data analyses, is crucial. Surface damage evolution is often accessed by imaging and subsequent image post-processing. In this work, we evaluated deep learning (DL) methodologies for semantic segmentation and different image processing approaches for quantitative slip trace characterization. Due to limited annotated data, a U-Net architecture was utilized. Three data sets of damage locations observed in scanning electron microscope (SEM) images of ferritic steel, martensitic steel, and copper specimens were prepared. In order to allow the developed models to cope with material-specific damage morphology and imaging-induced variance, a customized augmentation pipeline for the input images was developed. Material domain generalizability of ferritic steel and conjunct material trained models were tested successfully. Multiple image processing routines to detect slip trace orientation (STO) from the DL segmented extrusion areas were implemented and assessed. In conclusion, generalization to multiple materials has been achieved for the DL methodology, suggesting that extending it well beyond fatigue damage is feasible

Anomaly Detection in Textured Surfaces

Detecting anomalies in textured surfaces is an important and interesting problem that has practical applications in industrial defect detection and infrastructure asset management with a lot of potential financial benefits. The main challenges in this task are that the definition of anomaly changes from domain to domain, even noise can differ from the normal data but should not be classified as an anomaly, lack of labelled datasets and a limited number of anomalous instances. In this research, we have explored weak supervision and network-based transfer learning for anomaly detection. We developed a technique called AnoNet, which is a novel and compact fully convolutional network architecture capable of learning to detect the actual shape of anomalies not only from weakly labelled data but also from a limited number of examples. It uses a unique filter bank initialization technique that allows faster training. For a HxWx1 input image, it outputs a HxWx1 segmentation mask and also generalises to similar anomaly detection tasks. AnoNet on an average across four challenging datasets achieved an impressive F1 Score and AUROC value of 0.98 and 0.94 respectively. The second approach involved the use of network-based transfer learning for anomaly detection using pre-trained CNN architectures. In this investigation, fixed feature extraction and full network fine tuning approaches were explored. Results on four challenging datasets showed that the full network fine tuning based approach gave promising results with an average F1 Score and AUROC values of 0.89 and 0.98 respectively. While we have successfully explored and developed a method each for anomaly detection with weak supervision and supervision from a limited number of samples, research potential exists in semi-supervised and unsupervised anomaly detection

Sistema robotizado basado en visión artificial para envasado de hortalizas.

En el siguiente trabajo fin de grado se desarrolla un sistema robotizado basado en visión artificial para envasado de hortalizas, donde, mediante imágenes obtenidas por webcam, se clasificará las hortalizas en función de su tamaño. Se diseñará un gripper que garantice el agarre de estas sin dañarlas, y se colocarán en distintos lugares mediante un brazo robot dependiendo de la clasificación obtenida anteriormente. Se decide realizar este proyecto con el fin de agilizar el envasado de hortalizas y verduras en plantas que aún siguen realizando este proyecto manualmente

Clasificación de imágenes mediante algoritmos de Deep Learning: Mascarillas de COVID-19

La pandemia mundial causada por la Covid-19 ha provocado un antes y un después en nuestras vidas, tanto, que ahora llevar mascarilla con el fin de frenar su contagio es algo primordial e impensable en determinadas ocasiones. A raíz de la desesperación originada por este virus se ha incrementado el interés en métodos científicos que puedan ayudar a estabilizar y controlar la situación. Este proyecto gira en torno a este tema tan actual, ya que persigue alcanzar una eficiente clasificación de imágenes según se lleve mascarilla o no, así como diferenciando también si se lleva de forma incorrecta. Para desarrollarlo, se han empleado redes neuronales convolucionales basadas en Deep Learning, algunos populares paquetes básicos de aprendizaje automático como es Keras o TensorFlow y el lenguaje de programación Python 3.6. Los resultados obtenidos en este experimento, usando las herramientas presentadas y trabajando para lograr un ajuste de parámetros que optimice el resultado, terminan con una precisión del algoritmo máxima de un 95.31 % para el diseño final seleccionado.The global pandemic caused by Covid-19 has caused a before and after in our lives, so much that now wearing a mask in order to stop its contagion is essential and unthinkable in certain occasions. As a result of the desperation caused by this virus, there has been an increased interest in scientific methods that could help stabilize and control the situation. This project revolves around this very current topic, since it seeks to achieve an efficient images classification depending on whether a mask is worn or not, as well as differentiating whether it is worn incorrectly. To develop it, convolutional neural networks based on Deep Learning, some popular basic machine learning packages such as Keras or TensorFlow and the Python 3.6 programming language have been used. The results obtained in this experiment, using the tools presented and working to achieve a parameter adjustment that optimizes the result, ends with a maximum algorithm precision of 95.31%.Universidad de Sevilla. Grado en Ingeniería de las Tecnologías de Telecomunicació

Objektive und reproduzierbare Gefügeklassifizierung niedriglegierter Stähle

Ziel der vorliegenden Arbeit ist die Entwicklung einer objektiven und reproduzierbaren Gefügeklassifizierung niedriglegierter Stähle. Hierfür wird mit den in der Informatik zur Verfügung stehenden Methoden des maschinellen Lernens ein Arbeitsablauf für eine Klassifizierung der drei Gefügebestandteile Perlit, Bainit und Martensit erarbeitet. Als Grundlage für das Klassifizierungsmodell wird das Stützvektorverfahren (Support Vector Machine, SVM) genutzt, welches auf einen Merkmalsdatensatz der drei Klassen angewendet wurde. Für den Aufbau der Datenbank werden verschiedene Gefügemerkmale aus korrelativen Licht- und Elektronenmikroskopaufnahmen verwendet. Die Merkmalsdatenbank beinhaltet form- und größenbeschreibende Parameter sowie pixelbasierte Merkmale, die aus der Bildtextur der Mikroskopaufnahmen extrahiert werden. Der Einfluss der Datenvorverarbeitung und -aufteilung auf die Klassifizierungsergebnisse werden untersucht. Neben dem Aufbau eines validen Klassifizierungsprozesses liegt der Fokus auf der Weiterentwicklung und Identifizierung der für die Klassifizierung entscheidenden, signifikanten Gefügemerkmale. Für die aufgebaute Datenbasis können Klassifizierungsgenauigkeiten von bis zu 97 % für die vordefinierten Klassen erreicht werden. Die Methodik des vorgestellten Ansatzes der Gefügeklassifizierung kann im Bereich der Stahlwerkstoffe erweitert und auf andere Werkstoffklassen übertragen werden.The aim of this thesis is to develop an objective and reproducible microstructure classification of low-alloy steels. For this purpose, a workflow for the classification of the three microstructural constituents pearlite, bainite and martensite is established using the methods of machine learning available in computer science. The classification model is based on the support vector machine (SVM), which has been applied to a feature dataset of these three classes. To build up the database, various microstructural features extracted from correlative light and electron microscope images are used. The feature database contains shape and size describing parameters as well as pixelbased features, which are extracted from the image texture of the microscope images. The influence of data pre-processing and data splitting on classification results is investigated. In addition to the design of a valid classification process, the focus is on the development and identification of the significant microstructural features which are relevant for the classification. It is possible to achieve classification accuracies of up to 97 % for the predefined classes using the generated database. The methodology of the approach presented can be extended in the field of steel materials and be transferred to other material classes