Defect detection and segmentation inmultivariate image streams

University essay from KTH/Skolan för datavetenskap och kommunikation (CSC)

Author: BjÖrn Hegstam; [2013]

Keywords: ;

Abstract: OptoNova is a world leading producer of inspection systems for quality control of surfaces and edges at high rates. They develop their own sensor systems and software and have taken an interest in investigating the possibility of using methods from machine learning to make better use of the available sensor data. The purpose of this project was to develop a method for finding surface defects based on multivariate images. A previous Master’s project done at OptoNova had shown promising results when applying machine learning methods to inspect the sides of kitchen cabinet doors. The model developed for that project was based around using a Difference of Gaussians scale-space. That was used as a starting ground for the work presented here, with changes made in order to focus on texture defects on flat surfaces. The final model works by creating a Laplacian image pyramid from a source image. Each pyramid level is processed by a trained image model that, given a multivariate image, produces a greyscale image indicating defect areas. The outputs of all image models are scaled to the same size and averaged together. This gives the final probability map indicating what parts of the sample are defective. The image models consists of a feature extractor, extracting one feature per pixel, and a feature model, which in this project was a Gaussian mixture model. The model was built in a modular fashion, making it easy to use different features and feature models. Tests showed the pyramid model to perform better than the previous model. Defects characterised by noticeable differences in surface texture gave excellent results, while defects only indicated by slight changes in intensity of the normal texture were generally not found. It was concluded that the developed model shows potential, but more work needs to be done. More tests need to be run using larger data sets and samples with different texture types, such as wooden surfaces.

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