Open AccessA method of searching and marking artifacts in images applying detection and segmentation algorithms
Author(s) -
Andrey Kitenko,
AUTHOR_ID
Publication year - 2021
Publication title -
sistemy analiza i obrabotki dannyh
Language(s) - English
Resource type - Journals
eISSN - 2782-215X
pISSN - 2782-2001
DOI - 10.17212/2782-2001-2021-4-7-18
Subject(s) - computer science , segmentation , convolutional neural network , artificial intelligence , artifact (error) , process (computing) , pattern recognition (psychology) , artificial neural network , basis (linear algebra) , selection (genetic algorithm) , image processing , image (mathematics) , machine learning , computer vision , geometry , mathematics , operating system
The paper explores the possibility of using neural networks to single out target artifacts on different types of documents. Numerous types of neural networks are often used for document processing, from text analysis to the allocation of certain areas where the desired information may be contained. However, to date, there are no perfect document processing systems that can work autonomously, compensating for human errors that may appear in the process of work due to stress, fatigue and many other reasons. In this work, the emphasis is on the search and selection of target artifacts in drawings, in conditions of a small amount of initial data. The proposed method of searching and highlighting artifacts in the image consists of two main parts, detection and semantic segmentation of the detected area. The method is based on training with a teacher on marked-up data for two convolutional neural networks. The first convolutional network is used to detect an area with an artifact, in this example YoloV4 was taken as the basis. For semantic segmentation, the U-Net architecture is used, where the basis is the pre-trained Efficientnetb0. By combining these neural networks, good results were achieved, even for the selection of certain handwritten texts, without using the specifics of building neural network models for text recognition. This method can be used to search for and highlight artifacts in large datasets, while the artifacts themselves may be different in shape, color and type, and they may be located in different places of the image, have or not have intersection with other objects.