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 başlığı | Deep Learning Based, Real-Time Object Detection for Autonomous Driving |
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Orijinal dil | Türkçe |
Ana bilgisayar yayını başlığı | 2020 28th Signal Processing and Communications Applications Conference, SIU 2020 - Proceedings |
Yayınlayan | Institute of Electrical and Electronics Engineers Inc. |
ISBN (Elektronik) | 9781728172064 |
DOI'lar | |
Yayın durumu | Yayınlandı - 5 Eki 2020 |
Etkinlik | 28th Signal Processing and Communications Applications Conference, SIU 2020 - Gaziantep, Turkey Süre: 5 Eki 2020 → 7 Eki 2020 |
Yayın serisi
Adı | 2020 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 |
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Ülke/Bölge | Turkey |
Şehir | Gaziantep |
Periyot | 5/10/20 → 7/10/20 |
Bibliyografik not
Publisher Copyright:© 2020 IEEE.
Keywords
- Autonomous Driving
- Computer Vision
- Deep Learning
- Kalman Filter
- Object Detection