Abstract
The model of object detector and the criterion of leaning effectiveness of the model were proposed. The model contains 7 first modules of the convolutional Squeezenet network, two convolutional multiscale layers and the informationextreme classifier. The multiplicative convolution of the particular criteria that takes into account the effectiveness of detection of objects in the image and accuracy of the classification analysis was considered as the criterion of learning effectiveness of the model. In this case, additional use of the orthogonal matching pursuit algorithm in calculating highlevel features makes it possible to increase the accuracy of the model by 4 %. The training algorithm of object detector under conditions of a small size of labeled training datasets and limited computing resources available on board of a compact unmanned aerial vehicle was developed. The essence of the algorithm is to adapt the highlevel layers of the model to the domain application area, based on the algorithms of growing sparse coding neural gas and simulated annealing. Unsupervised learning of highlevel layers makes it possible to use effectively the unlabeled datasets from the domain area and determine the required number of neurons. It is shown that in the absence of fine tuning of convolutional layers, 69 % detection of objects in the images of the test dataset Inria Aerial Image was ensured. In this case, after fine tuning based on the simulated annealing algorithm, 95 % detection of the objects in test images is ensured. It was shown that the use of unsupervised pretraining makes it possible to increase the generalizing ability of decision rules and to accelerate the iteration process of finding the global maximum during supervised learning on the dataset of limited size. In this case, the overfitting effect is eliminated by optimal selection of the value of hyperparameter, characterizing the measure of coverage of the input data of by network neurons.
Highlights
Unmanned aviation is widely used in the tasks of inspection of technological and residential facilities, protection and reconnaissance activities, as well as in the sphere of transportation of small size loads
One of the ways to increase the functional efficiency of the unmanned aerial vehicle (UAV) is to introduce technologies of artificial in
To ensure the noise immunity and informativeness of feature representation, it is proposed to calculate the activation of each feature map pixel based of the algorithm is orthogonal matching pursuit (OMP) with the ReLU function [12, 14]
Summary
Unmanned aviation is widely used in the tasks of inspection of technological and residential facilities, protection and reconnaissance activities, as well as in the sphere of transportation of small size loads. The layers of high-level feature representation of observations require learning from scratch In this case, it is difficult to estimate in advance the required number of convolutional filters in each convolutional layer, which is why the promising approach to learning convolutional filters is to use the principles of growing neural gas, which makes it possible to determine automatically the necessary number of neurons. It is difficult to estimate in advance the required number of convolutional filters in each convolutional layer, which is why the promising approach to learning convolutional filters is to use the principles of growing neural gas, which makes it possible to determine automatically the necessary number of neurons In this case, the layers of decision rules for the detector and feature extraction layers require fine-tuning, which is typically implemented based on the modifications of the error backpropagation algorithm [2, 3]. That is why the research, aimed at enhancing the effectiveness of learning the detector of objects under conditions of limited computing resources and learning data, is relevant
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
