Otonom Araclar icin Derin Ogrenme Tabanli, Gercek Zamanli Nesne Tespiti

Gamze Akyol, Alperen Kantarci, Ali Eren Celik, Abdullah Cihan Ak

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde BölümKonferans katkısıbilirkişi

14 Atıf (Scopus)

Özet

One of the active research topics that maintains its popularity in the field of Computer Vision is the problem of object detection in autonomous cars. Since object detection is a difficult problem, high performance solutions do not work very quickly. Similarly, real-time solutions make compromise on performance. However, due to the nature of autonomous driving, object detection systems must perform in real time and high performance. In this study, Tiny YOLOv3, one of the most successful object detection architectures, was combined with one of the classical object tracking methods, the Kalman filter. A small and real-time object detection system, which increases the model's accuracy without losing its speed, is proposed.

Tercüme edilen katkı başlığıDeep Learning Based, Real-Time Object Detection for Autonomous Driving
Orijinal dilTürkçe
Ana bilgisayar yayını başlığı2020 28th Signal Processing and Communications Applications Conference, SIU 2020 - Proceedings
YayınlayanInstitute of Electrical and Electronics Engineers Inc.
ISBN (Elektronik)9781728172064
DOI'lar
Yayın durumuYayınlandı - 5 Eki 2020
Etkinlik28th Signal Processing and Communications Applications Conference, SIU 2020 - Gaziantep, Turkey
Süre: 5 Eki 20207 Eki 2020

Yayın serisi

Adı2020 28th Signal Processing and Communications Applications Conference, SIU 2020 - Proceedings

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???event.eventtypes.event.conference???28th Signal Processing and Communications Applications Conference, SIU 2020
Ülke/BölgeTurkey
ŞehirGaziantep
Periyot5/10/207/10/20

Bibliyografik not

Publisher Copyright:
© 2020 IEEE.

Keywords

  • Autonomous Driving
  • Computer Vision
  • Deep Learning
  • Kalman Filter
  • Object Detection

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