Usage of fully convolutional network with clustering for traffic light detection

Dmitry Yudin, Dmitry Slavioglo

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

10 Citations (Scopus)

Abstract

In this paper we consider a traffic light detector constructed on the basis of a fully convolutional neural network for segmenting traffic lights on image and subsequent clustering, which allows us to obtain bounding boxes for traffic lights. The proposed approach is compared with one of the most effective object detectors - Single Shot Multibox detector (SSD). We implemented algorithms for objects detection on an embedded system based on the NVidia Jetson TX2 platform. Traffic light detection recall for the proposed approach is better than SSD and higher than 0.9 on both the testing and training samples from relatively small data set (500 training images and 107 testing images). Time of traffic lights detection on one frame is about 50 ms. The results of the traffic light detection prove the possibility of applying the approach based on fully convolutional neural network with clustering for embedded autonomous vehicle control systems and driver assistance systems.

Original languageEnglish
Title of host publication2018 7th Mediterranean Conference on Embedded Computing, MECO 2018 - Including ECYPS 2018, Proceedings
EditorsLech Jozwiak, Budimir Lutovac, Drazen Jurisic, Radovan Stojanovic
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-6
Number of pages6
ISBN (Electronic)9781538656822
DOIs
Publication statusPublished - 6 Jul 2018
Externally publishedYes
Event7th Mediterranean Conference on Embedded Computing, MECO 2018 - Budva, Montenegro
Duration: 10 Jun 201814 Jun 2018

Publication series

Name2018 7th Mediterranean Conference on Embedded Computing, MECO 2018 - Including ECYPS 2018, Proceedings

Conference

Conference7th Mediterranean Conference on Embedded Computing, MECO 2018
Country/TerritoryMontenegro
CityBudva
Period10/06/1814/06/18

Keywords

  • clustering
  • deep learning
  • detection
  • embedded system
  • fully convolutional network
  • image recognition
  • single shot multibox detector
  • traffic light

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