SDA: A NOVEL SKEWED-DEEP-ARCHITECTURE FOR VEHICLE MOTION DETECTION IN DRIVING VIDEOS

Tansu Temel; Mehmet Kılıçarslan; Yaşar Hoşcan

doi:10.17780/ksujes.1358512

EN TR

SDA: A NOVEL SKEWED-DEEP-ARCHITECTURE FOR VEHICLE MOTION DETECTION IN DRIVING VIDEOS

Abstract

Collision avoidance mechanisms are important topics for studies in the field of autonomous vehicles. We could obtain prior information about the collision from the movement angles of vehicles. Therefore, it is important issue to learn the movement angles of vehicles in motion. In the study, an architectural model is developed that learns the horizontal movement angles of vehicles to form a base for collision warning systems. YOLOv3 is modified and used on motion profiles. Thanks to the learned angle values, also the bounding boxes match the traces in the motion profiles smoothly. The results obtained have a mAP value of 79% and an operating speed of 36 FPS. These results are better than when trained on motion profiles of the YOLOv3 architecture. In addition, the use of the new architecture on motion profiles and factors such as noise and bad weather in the image do not adversely affect the results. With these features, a fundamental step has been taken for anti-collision systems.

Keywords

Supporting Institution

Scientific and Technological Research Council of Turkey (TÜBİTAK)

Project Number

122E586

Thanks

This work was supported by the Scientific and Technological Research Council of Turkey (TÜBİTAK) with projects numbered 122E586.

References

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Details

Primary Language

English

Subjects

Computer Vision , Image Processing , Pattern Recognition , Deep Learning

Journal Section

Research Article

Authors

Tansu Temel ^*
0000-0002-8359-1146
Türkiye

Mehmet Kılıçarslan
0000-0002-7212-5262
Türkiye

Yaşar Hoşcan
0000-0003-0789-6025
Türkiye

Publication Date

March 3, 2024

Submission Date

September 12, 2023

Acceptance Date

November 24, 2023

Published in Issue

Year 1970 Volume: 27 Number: 1

DOI

https://doi.org/10.17780/ksujes.1358512

IZ

https://izlik.org/JA69CF97SK

Cite

RIS / Bibtex

APA

Temel, T., Kılıçarslan, M., & Hoşcan, Y. (2024). SDA: A NOVEL SKEWED-DEEP-ARCHITECTURE FOR VEHICLE MOTION DETECTION IN DRIVING VIDEOS. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 27(1), 92-104. https://doi.org/10.17780/ksujes.1358512

Cited By

Two-Strea[M] YOLOV8: Object and Motion Detection in Driving Videos

IEEE Transactions on Intelligent Vehicles

https://doi.org/10.1109/TIV.2024.3448631

An end-to-end deep learning model for vehicle speed estimation using a single camera

Internet of Things

https://doi.org/10.1016/j.iot.2026.101903

SDA: A NOVEL SKEWED-DEEP-ARCHITECTURE FOR VEHICLE MOTION DETECTION IN DRIVING VIDEOS

SDA: A NOVEL SKEWED-DEEP-ARCHITECTURE FOR VEHICLE MOTION DETECTION IN DRIVING VIDEOS

Abstract

Keywords

EDM: SÜRÜŞ VIDEOLARINDA ARAÇ HAREKET ALGILAMASI IÇIN YENI BIR EĞIK-DERIN-MIMARI

Öz

Anahtar Kelimeler

Supporting Institution

Project Number

Thanks

References

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Subjects

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Submission Date

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Cited By

Two-Strea[M] YOLOV8: Object and Motion Detection in Driving Videos

An end-to-end deep learning model for vehicle speed estimation using a single camera