Combination of computer vision detection and segmentation for autonomous driving

Yu Ho Tseng, Shau Shiun Jan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Citations (Scopus)

Abstract

Most existing deep learning networks for computer vision attempt to improve the performance of either semantic segmentation or object detection. This study develops a unified network architecture that uses both semantic segmentation and object detection to detect people, cars, and roads simultaneously. To achieve this goal, we create an environment in the Unity engine as our dataset. We train our proposed unified network that combines segmentation and detection approaches with the simulation dataset. The proposed network can perform end-to-end prediction and performs well on the tested dataset. The proposed approach is also efficient, processing each image in about 30 ms on an NVIDIA GTX 1070.

Original languageEnglish
Title of host publication2018 IEEE/ION Position, Location and Navigation Symposium, PLANS 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1047-1052
Number of pages6
ISBN (Electronic)9781538616475
DOIs
Publication statusPublished - 2018 Jun 5
Event2018 IEEE/ION Position, Location and Navigation Symposium, PLANS 2018 - Monterey, United States
Duration: 2018 Apr 232018 Apr 26

Publication series

Name2018 IEEE/ION Position, Location and Navigation Symposium, PLANS 2018 - Proceedings

Other

Other2018 IEEE/ION Position, Location and Navigation Symposium, PLANS 2018
CountryUnited States
CityMonterey
Period18-04-2318-04-26

All Science Journal Classification (ASJC) codes

  • Automotive Engineering
  • Aerospace Engineering
  • Control and Optimization

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