Abstract
Semantic segmentation is one of the important ways of extracting information about objects in images. State of the art neural network algorithms allow to perform highly accurate semantic segmentation of images, including aerial photos. However, in most of the works authors use high-quality low-noise images. In this work, we study the ability of neural networks to correctly segment images with intensive uncorrelated Gaussian noise. The study brings us three main conclusions. Firstly, it demonstrates that neural network algorithms are capable of working with extreme image distortions without using additional filtration or image recovery techniques. Secondly, the experiments quantitatively show that distortion intensity can be negated with increased training set size. Such process is similar to model’s quality improvement and generalization due to training dataset enlargement. Finally, we quantitatively demonstrate how image aggregation techniques affect training with noised data.
Highlights
IntroductionThere is an increased interest in the field of computer vision
Nowadays, there is an increased interest in the field of computer vision
There is an increased interest in the field of computer vision. This is due to significant progress in the field of deep neural networks (DNN) design, increase in available computational resources, as well as availability of huge databases of labeled data
Summary
There is an increased interest in the field of computer vision This is due to significant progress in the field of deep neural networks (DNN) design, increase in available computational resources, as well as availability of huge databases of labeled data. The combination of these factors allows us to solve a wide variety of tasks that were previously inaccessible to classical computer vision algorithms. Along with the range of tasks expansion, we naturally encounter questions about limit of the applicability of given methods Such limitations can be determined by the problem formulation, available computational power, DNN building and training techniques, data quality, etc. We suggest ways of negative effects reduction with image aggregation methods
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