Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders

Convolutional autoencoders have emerged as popular methods for unsupervised defect segmentation on image data. Most commonly, this task is performed by thresholding a pixel-wise reconstruction error based on an $\ell^p$ distance. This procedure, however, leads to large residuals whenever the reconstruction encompasses slight localization inaccuracies around edges. It also fails to reveal defective regions that have been visually altered when intensity values stay roughly consistent. We show that these problems prevent these approaches from being applied to complex real-world scenarios and that it cannot be easily avoided by employing more elaborate architectures such as variational or feature matching autoencoders. We propose to use a perceptual loss function based on structural similarity which examines inter-dependencies between local image regions, taking into account luminance, contrast and structural information, instead of simply comparing single pixel values. It achieves significant performance gains on a challenging real-world dataset of nanofibrous materials and a novel dataset of two woven fabrics over the state of the art approaches for unsupervised defect segmentation that use pixel-wise reconstruction error metrics

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

Building Disease Detection Algorithms with Very Small Numbers of Positive Samples

Although deep learning can provide promising results in medical image analysis, the lack of very large annotated datasets confines its full potential. Furthermore, limited positive samples also create unbalanced datasets which limit the true positive rates of trained models. As unbalanced datasets are mostly unavoidable, it is greatly beneficial if we can extract useful knowledge from negative samples to improve classification accuracy on limited positive samples. To this end, we propose a new strategy for building medical image analysis pipelines that target disease detection. We train a discriminative segmentation model only on normal images to provide a source of knowledge to be transferred to a disease detection classifier. We show that using the feature maps of a trained segmentation network, deviations from normal anatomy can be learned by a two-class classification network on an extremely unbalanced training dataset with as little as one positive for 17 negative samples. We demonstrate that even though the segmentation network is only trained on normal cardiac computed tomography images, the resulting feature maps can be used to detect pericardial effusion and cardiac septal defects with two-class convolutional classification networks

A comparative study of image processing thresholding algorithms on residual oxide scale detection in stainless steel production lines

The present work is intended for residual oxide scale detection and classification through the application of image processing techniques. This is a defect that can remain in the surface of stainless steel coils after an incomplete pickling process in a production line. From a previous detailed study over reflectance of residual oxide defect, we present a comparative study of algorithms for image segmentation based on thresholding methods. In particular, two computational models based on multi-linear regression and neural networks will be proposed. A system based on conventional area camera with a special lighting was installed and fully integrated in an annealing and pickling line for model testing purposes. Finally, model approaches will be compared and evaluated their performance..Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

A computerised data handling procedure for defect detection and analysis for large area substrates manufactured by roll-to-roll process

The development of optical on-line/in-process surface inspection and characterisation systems for flexible roll to roll (R2R) thin film barriers used for photo-voltaic (PV) modules is a core research goal for the EU funded NanoMend project. Micro and nano scale defects in the ALD (atomic layer deposition) Al2O3 barrier coating produced by R2R techniques can affect the PV module efficiency and lifespan. The presence of defects has been shown to have a clear correlation with the water-vapour-transmission-rate (WVTR). Hence, in order to improve the PV cell performance and lifespan the barrier film layer must prevent water vapour ingress. One of the main challenges for the application of in process metrology is how to assess large and multiple measurement data sets obtained from an in process optical instrument. Measuring the surface topography over large area substrates (approximately 500 mm substrate width) with a limited field-of-view (FOV) of the optical instrument will produce hundreds/thousands of measurement files. Assessing each file individually to find and analyse defects manually is time consuming and impractical. This paper reports the basis of a computerised solution to assess these files by monitoring and extracting areal surface topography parameters. Comparing parameter values to an experimentally determined threshold value, obtained from extensive lab-based measurement of Al2O3 ALD coated films, can indicate the existence of the defects within a given FOV. This process can be repeated automatically for chosen parameters and the existence of defects can be indicated for the entire set of measurement files spontaneously without interaction from the inspector. A running defect log and defect statistics associated with the captured set of data files can be generated. This paper outlines the implementation of the auto-defect logging using advanced areal parameters, and its application in a proof of concept system at the Center for Process Innovation (UK) is discussed