On Image Augmentation

The paper considers methods of natural image augmentation, i.e. those whose application results are close to natural impacts on environmental objects that machine learning models may encounter in industrial applications: the influence of weather conditions; operating features or malfunctions of device cameras, etc. The paper presents a taxonomy of methods of natural image augmentation, which includes weather artifacts, camera artifacts, and background substitution for the main object in the image. Existing software libraries for image augmentation are considered in detail, and their shortcomings and limitations are described. The architecture and implementation of a new open library for image augmentation are presented, and the results of its testing on specialized datasets are given. - On Natural Image Augmentation to Increase Robustness of Machine Learning Models