Open AccessResearch of recognition accuracy of dangerous and safe x-ray baggage images using neural network transfer learning
Author(s) -
Nikita Andriyanov,
Al K. Volkov,
An K. Volkov,
А. А. Гладких
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1061/1/012002
Subject(s) - reset (finance) , computer science , convolutional neural network , artificial neural network , transfer of learning , artificial intelligence , sample (material) , pattern recognition (psychology) , transfer (computing) , class (philosophy) , machine learning , chemistry , chromatography , parallel computing , financial economics , economics
The article considers the use of neural networks to solve the problem of recognizing dangerous and safe objects carried in the luggage of airport passengers. A comparative analysis is performed to define the accuracy achieved on the test sample for different convolutional neural networks. It also explores the influence of various regularizations on the accuracy of a two-class classification. The increased probability of correct recognition is achieved due to augmentation, reset weights and saturation of the network. The method of transfer training is used to increase the efficiency of the recognizer. In this case, a study was carried out for the transfer of various neural networks.