Abstract
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.
| Translated title of the contribution | Deep Learning Based, Real-Time Object Detection for Autonomous Driving |
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| Original language | Turkish |
| Title of host publication | 2020 28th Signal Processing and Communications Applications Conference, SIU 2020 - Proceedings |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| ISBN (Electronic) | 9781728172064 |
| DOIs | |
| Publication status | Published - 5 Oct 2020 |
| Event | 28th Signal Processing and Communications Applications Conference, SIU 2020 - Gaziantep, Turkey Duration: 5 Oct 2020 → 7 Oct 2020 |
Publication series
| Name | 2020 28th Signal Processing and Communications Applications Conference, SIU 2020 - Proceedings |
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Conference
| Conference | 28th Signal Processing and Communications Applications Conference, SIU 2020 |
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| Country/Territory | Turkey |
| City | Gaziantep |
| Period | 5/10/20 → 7/10/20 |
Bibliographical note
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