Neural networks modification for solving the traffic signs detection problem

The paper deals with the implementation of the traffic signs detection model on the deep neural network basis. The complexity of the problem lies in the detected objects size. The paper considers the use of modern approaches to the deep neural networks Mobile Net v2 and Tiny YOLO v3 implementation t...

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Bibliographic Details
Published inIOP conference series. Materials Science and Engineering Vol. 695; no. 1; pp. 12024 - 12029
Main Authors Devyatkin, A.V., Filatov, D. M.
Format Journal Article
LanguageEnglish
Published Bristol IOP Publishing 01.11.2019
Subjects
Artificial neural networks
deep neural network
detection
MobileNet
Neural networks
Object recognition
Traffic models
Traffic signs
Training
YOLO
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Summary:The paper deals with the implementation of the traffic signs detection model on the deep neural network basis. The complexity of the problem lies in the detected objects size. The paper considers the use of modern approaches to the deep neural networks Mobile Net v2 and Tiny YOLO v3 implementation to solve the detection problem in real time. Also, a modification of the considered networks is proposed, which allows increasing the Average Precision index by more than 20%. For the networks training GPGPU NVIDIA 1080 and a signs images set called Russian traffic sign images dataset - RTSD which includes more than 15,000 frames for training and more than 3,000 frames for carrying out testing were used.
ISSN:1757-8981
1757-899X
DOI:10.1088/1757-899X/695/1/012024