Image Augmentation in Generation of Real-Life Disturbances : An Evaluation of Image Augmentation Techniques for Log-end Identification

Sammanfattning: Image augmentation is a field that covers the subject area of altering existing data to create more for the use of model training processes. It may be seen as the practice of expanding upon existing data using a range of techniques that employ transformations to improve the diversity of training sets when applied to machine learning. In our case of image recognition, triplet loss is utilised to pair a reference image to a matching and non-matching input. However, since there are many single images, augmentation techniques are relied upon to expand upon our data set to improve the recognition of images and create true positives. True positives are created using standard augmentation techniques like perspective transformation, contrast, cropping, and more. Despite this, the same images may undergo other types of alterations, natural disturbances such as transformations and warping, that are not captured by standard augmentation techniques. Such instances constitute to the variance in identification. Therefore, the analysis of augmentations by artificial intelligence (AI) based recognition is proposed; AI is used in order to identify what contributes to realistic disturbances of single images that better imitate real-life transformations. Analysing existing standard image augmentation techniques should provide further insight within this scope, as to better determine ways of emulating natural disturbances, and the formulation of non-standard practices in tandem. How this is done is by the use of an image's identity, the pixels it's comprised of and their distributions. Through a methodology of inspecting image identities, the breaking down of augmentations, and the inquiry into practices of non-standard image augmentation techniques, we detect the variance in accuracy of generated models, analysing the comprised data sets. Our results show that augmentations improve accuracy on a basis of variance and divergence from the original image. Subsequent discussion expands upon the identities of images and how augmentations must still resemble true positives, with the potential of an augmentation gauged by its influence on the rate of growth and highest accuracy of a model.