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14. Microscopic Image Segmentation for Automated Inspection of Satellite Components

Author

Bohlig, D., Leutert, F., Kempf, F., Möschwitzer, D., and Schilling, K.

Subjects
Optical inspection, Deep Learning, DGLR, Computer Vision, DLRK, Satellite assembly
Abstract

In the move towards image processing for Automated Integration and Testing (AIT) we explored defect detection on reflowed satellite PCBs using deep learning. To this end we utilized a convolutional neural network for semantic segmentation and subsequent instance segmentation for the detection of surface mounted devices, pollution and defects. A dataset of 16k labeled instances including devices, solder connections, solderballs, bridges and tombstoned components was created from our satellite fleet PCBs to train the network. The images were recorded using microscopes and industrial cameras and labeled using an active learning approach with human experts annotating the initial data. Then, the partially trained network labeled additional data with experts supervising the process and correcting predictions where necessary.We explored k-fold cross validation as well as dropout based uncertainty estimation for the prediction of samples that meaningfully extend our training data. Further we evaluated the benefits of the implemented procedures and the annotation speedup from the network assisted annotation. The resulting inspection system was successfully integrated into a human-robot collaborative workspace to increase its production efficiency.

Published
2022
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21. Remote experiments with mobile-robot hardware via internet at limited link capacity

Author

Zeiger, F., Schmidt, M., and Schilling, K.

Subjects
Automation -- Study and teaching, Mechanization -- Study and teaching, Computation laboratories -- Design and construction, Distance education -- Technology application, Mobile robots -- Design and construction, Robotics -- Study and teaching, Virtual reality -- Usage, Virtual reality technology, Technology application, Business, Computers, Electronics, Electronics and electrical industries
Published
2009

37. Manganese-free chow, a refined non-invasive solution to reduce gastrointestinal signal for T1-weighted MRI of the mouse abdomen

Author

Kersemans, V, Wallington, SE, Allen, P, Gilchrist, S, Kinchesh, P, Browning, RJ, Vallis, K, Schilling, K, Holdship, P, Stork, L-A, and Smart, S

Abstract

Commercial mouse chow is designed to provide a complete, nutrient-rich diet, and it can contain upwards of 100 mg/kg manganese, an essential mineral. Manganese acts as a relaxation time shortening contrast agent for both T1 and T2, and where standard chow is hydrated in the gastrointestinal tract, bright signals are produced when using T1-weighted imaging (T1WI). As a result of peristalsis, gastrointestinal hyperintensities result in temporally unstable signals, leading to imageghosting and decreased resolution from that prescribed. To avoid the problem, various methods of gastrointestinal tract modulation including the use of intestinal cleansing with laxatives and dietary modulation have been reported. Here, dietary modulation has extended to use of a biologically innocuous, long-term change of diet. In this study, we report on the use of a commercially available manganese-free chow to improve image quality of the gastrointestinal tract. This manganese-free chow, apart from the omitted manganese which is available in tap water, is a complete diet and readily available. We investigated the time-dependent, diet-related gastrointestinal intensities on short TR, T1WI MRI; monitored body mass, food and water consumption and standard blood biochemistry analysis following diet change; and determined manganese concentration in blood plasma following a 5-day change to manganese-free chow. We show that the manganese-free chow presents a refinement to other gastrointestinal tract modulation as it avoids the need for invasive procedures for gut voiding and can be provided ad libitum so animals can be maintained with no need for prescribed diet change before imaging

Published
2019
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43. A Sum of Chebyshev Inequalities: 10687

Author

Owings,, James C., Dawson, Jeremy E., Chapman, Robin J., Nester, Darryl K., Agnew, R. A., Beebee, J., Callan, D., Garcia-Pelayo, R., Hernandez, V., Martin, J., Hickman, J., Jones, D., Kim, S. S., Krafft, O., Schaefer, M., Letac, G., Lindsey, J. H., Lossers, O. P., Poelladennou, K., Schilling, K., and Shemesh, M.

Published
2000
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