Open AccessTRAFFIC SIGNAL RECOGNITION ON AN IMAGE USING CONVOLUTIONAL NEURAL NETWORK
Author(s) -
Anton Holkin,
Nikita Andreyanov
Publication year - 2021
Language(s) - English
Resource type - Conference proceedings
DOI - 10.30987/conferencearticle_61c997ee7379d1.40655518
Subject(s) - computer science , convolutional neural network , signal (programming language) , artificial intelligence , interface (matter) , artificial neural network , deep learning , image (mathematics) , traffic signal , machine learning , pattern recognition (psychology) , computer vision , real time computing , operating system , bubble , maximum bubble pressure method , programming language
The purpose of this work is to develop an intelligent system for recognizing traffic signals. To achieve this, DetectNet was applied, using an interface for learning, which was developed by NVIDIA. With their help, the disadvantages of this approach were identified, and therefore it was necessary to consider another option for solving this problem.