Summary Embedded Deep Learning Object Detection Model Competition

Jiun In Guo, Chia Chi Tsai, Yong Hsiang Yang, Hung Wei Lin, Bo Xun Wu, Ted T. Kuo, Li Jen Wang

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

Abstract

The embedded deep learning object detection model competition in IEEE MMSP2019 focuses on the object detection for sensing technology in autonomous driving vehicles, which aims at detecting small objects in worse conditions through embedded systems. We provide a dataset with 89,002 annotated images for training and 1,500 annotated images for validation. We test participants' models through 6,000 testing images, which are separated into 3,000 for qualification and 3,000 for finals. There are 87 teams of participants registered this competition and 14 teams submitted the team composition. At last there are nine teams entering the final competition and five teams submitting their final models that can be realized in NVIDIA Jetson TX-2. At the end, only one team's model passed the target accuracy requirement for grading and became the champion of the contest, which the winner is team R.JD.

Original languageEnglish
Title of host publicationIEEE 21st International Workshop on Multimedia Signal Processing, MMSP 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728118178
DOIs
StatePublished - Sep 2019
Event21st IEEE International Workshop on Multimedia Signal Processing, MMSP 2019 - Kuala Lumpur, Malaysia
Duration: 27 Sep 201929 Sep 2019

Publication series

NameIEEE 21st International Workshop on Multimedia Signal Processing, MMSP 2019

Conference

Conference21st IEEE International Workshop on Multimedia Signal Processing, MMSP 2019
CountryMalaysia
CityKuala Lumpur
Period27/09/1929/09/19

Keywords

  • Autonomous driving vehicles
  • Embedded deep learning
  • Object detection

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