Abstract-We present a Max-Pooling Convolutional Neural Network approach for supervised steel defect classification. On a classification task with 7 defects, collected from a real production line, an error rate of 7% is obtained. Compared to SVM classifiers trained on commonly used feature descriptors our best net performs at least two times better. Not only we do obtain much better results, but the proposed method also works directly on raw pixel intensities of detected and segmented steel defects, avoiding further time consuming and hard to optimize ad-hoc preprocessing.
We present a fast algorithm for training MaxPooling Convolutional Networks to segment images. This type of network yields recordbreaking 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.
We introduce a Multi-Scale Pyramidal Pooling Network tailored to generic steel defect classification, featuring a novel pyramidal pooling layer at multiple scales and a novel encoding layer. Thanks to the former, the network does not require all images of a given classification task to be of equal size. The latter narrows the gap to bag-of-features approaches. On various benchmark datasets, we evaluate and compare our system to convolutional neural networks and state-of-the-art computer vision methods. We also present results on a real industrial steel defect classification problem, where existing architectures are not applicable as they require equally sized input images. Our method substantially outperforms previous methods based on engineered features. It can be seen as a fully supervised hierarchical bag-of-features extension that is trained online and can be fine-tuned for any given task.
Early changes to branching morphogenesis of the prostate are believed to lead to enlargement of the gland in adult life. However, it has not been possible to demonstrate directly that alterations to branching during the developmental period have a permanent effect on adult prostate size. In order to examine branching morphogenesis in a quantitative manner in neonatal mice, a combination of imaging and computational technology was used to detect and quantify branching using bone morphogenetic protein 4 haplo-insufficient mice that develop enlarged prostate glands in adulthood. Accurate estimates were made of six parameters of branching, including prostate ductal length and volume and number of main ducts, branches, branch points, and tips. The results show that the prostate is significantly larger on day 3, well before the emergence of the phenotype in older animals. The ventral prostate is enlarged because the number of main epithelial ducts is increased; enlargement of the anterior prostate in mutant animals occurs because there are more branches. These lobe-specific mechanisms underlying prostate enlargement indicate the complex nature of gland pathology in mice, rather than a simple increase in weight or volume. This method provides a powerful means to investigate the aetiology of prostate disease in animal models prior to emergence of a phenotype in later life.
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