Abstract: Various techniques facilitate the development of an image library that can be used to train and/or validate an automated visual inspection (AVI) model, such an AVI neural network for image classification. In one aspect, an arithmetic transposition algorithm is used to generate synthetic images from original images by transposing features (e.g., defects) onto the original images, with pixel-level realism. In other aspects, digital inpainting techniques are used to generate realistic synthetic images from original images. Deep learning-based inpainting techniques may be used to add, remove, and/or modify defects or other depicted features. In still other aspects, quality control techniques are used to assess the suitability of image libraries for training and/or validation of AVI models, and/or to assess whether individual images are suitable for inclusion in such libraries.

Abstract: In a method for enhancing accuracy and efficiency in automated visual inspection of vessels, a vessel containing a sample is oriented such that a line scan camera has a profile view of an edge of a stopper of the vessel. A plurality of images of the edge of the stopper is captured by the first line scan camera while spinning the vessel, where each image of the plurality of images corresponds to a different rotational position of the vessel. A two-dimensional image of the edge of the stopper is generated based on at least the plurality of images, and pixels of the two-dimensional image are processed, by one or more processors executing an inference model that includes a trained neural network, to generate output data indicative of a likelihood that the sample is defective.