EXPLORING OF SSD DEEP CONVOLUTIONAL NEURAL NETWORK FOR PEOPLE AND CAR DETECTION BY COMPUTER VISION SYSTEM OF MOBILE ROBOT

EXPLORING OF SSD DEEP CONVOLUTIONAL NEURAL NETWORK FOR PEOPLE AND CAR DETECTION BY COMPUTER VISION SYSTEM OF MOBILE ROBOT

S.R. Orlova
Russian State Scientific Center for Robotics and Technical Cybernetics (RTC), Laboratory Assistant Engineer, 21, Tikhoretsky pr., Saint-Petersburg, 194064, Russia, tel.: 7(911)005-31-30, This email address is being protected from spambots. You need JavaScript enabled to view it.

A. Komarov
Peter the Great Saint-Petersburg Polytechnical University (SPbPU), Student, 29, Politekhnicheskaya ul., Saint-Petersburg, 195251, Russia, tel.: +7(962)984-65-17, This email address is being protected from spambots. You need JavaScript enabled to view it.

Received 22 February 2018

Abstract
The paper reviewes the structure and operational principles of the SSD (Single Shot MultiBox Detector) deep convolutional neural network and describes the results of experimental research on the possibility of using this network for people and car detection by computer vision system of the mobile robot. The research showed that the SSD network can be used for this task, but it does not have very high accuracy and speed. The accuracy of the network was improved by modifying (histogram equalization) of input images and network learning. The research concluded that it is necessary to build own database for learning and to refine the network architecture for further efficiency and effectiveness improvement of the method.

Key words
Neural network, convolutional neural network, deep learning, object detection, computer vision system.

Bibliographic description
Orlova, S. and Komarov, A. (2018). Exploring of SSD deep convolutional neural network for people and car detection by computer vision system of mobile robot. Robotics and Technical Cybernetics, 2(19), pp.21-25.

UDC identifier:
004.896:004.832

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Editorial office address: 21, Tikhoretsky pr., Saint-Petersburg, Russia, 194064, tel.: +7(812) 552-13-25 e-mail: zheleznyakov@rtc.ru