Development, research of an algorithm for detecting small objects based on convolutional neural networks

Abstract

The unsatisfactory accuracy of detecting small objects in images of convolutional neural networks is associated with a lack of feature information characterizing small objects, which increases the likelihood of neural network retraining. To increase the feature space, information about the movement of dynamic objects is used. Movement identification of moving objects is based on the background subtraction method. Experimental studies show the superiority of the developed algorithm in the accuracy of detecting small objects in images compared to the original architecture of the convolutional neural network.