Specificities of data set preparation and neural network training for object recognition

Abstract

The paper presents the results of the research on the expediency of training a neural network on images of different clarity and brightness using unevenly distributed lighting on a working area with statically positioned system elements. The use of transfer learning for neural networks to improve the accuracy of object recognition was justified. The object recognition ability of a convolutional neural network while scaling the object relatively to the original was researched. The results of the research on the influence of lighting on the quality of object recognition by a trained network and the influence of background choice for a working area on the quality of object-based feature selection are presented. Based on the results obtained, recommendations for the preparation of individual datasets to improve the quality of training and further object recognition of convolutional neural networks through the elimination of unnecessary variables in images were provided.