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

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

Abstract

Çarpışma önleme mekanizmaları otonom araçlar alanındaki çalışmalar için önemli bir konudur. Araçların hareket açılarından çarpışma hakkında önceden bilgi alma imkanımız olmaktadır. Bu nedenle hareket halindeki araçların hareket açılarının öğrenilmesi önemli bir konudur. Çalışmada çarpışma uyarı sistemlerine temel oluşturmak amacıyla araçların yatay hareket açılarını öğrenen bir mimari model geliştirilmiştir. Başarılı derin öğrenme mimarilerinden biri olan YOLOv3 geliştirilerek elde edilen yeni mimari hareket profilleri üzerinde kullanıldı. Öğrenilen açı değerleri sayesinde sınırlayıcı kutular da, hareket profillerindeki izlerle tam olarak eşleşmektedir. Elde edilen sonuçlar %79 mAP değerine ve 36 FPS çalışma hızına sahiptir. Bu sonuçlar, saf YOLOv3 mimarisinin hareket profilleri üzerinde eğitildiklerinde elde edilen sonuçlardan daha iyidir. Yeni mimarinin hareket profillerinde kullanılması ile görüntüdeki gürültü, kötü hava gibi etkenler sonuçlarımızı olumsuz etkilememektedir. Bu özellikleri ile çarpışma önleyici sistemler için önemli bir adım atılmıştır.

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.

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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 2024 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

AMA

1.Temel T, Kılıçarslan M, Hoşcan Y. SDA: A NOVEL SKEWED-DEEP-ARCHITECTURE FOR VEHICLE MOTION DETECTION IN DRIVING VIDEOS. KSU J. Eng. Sci. 2024;27(1):92-104. doi:10.17780/ksujes.1358512

Chicago

Temel, Tansu, Mehmet Kılıçarslan, and Yaşar Hoşcan. 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.

EndNote

Temel T, Kılıçarslan M, Hoşcan Y (March 1, 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.

IEEE

[1]T. Temel, M. Kılıçarslan, and Y. Hoşcan, “SDA: A NOVEL SKEWED-DEEP-ARCHITECTURE FOR VEHICLE MOTION DETECTION IN DRIVING VIDEOS”, KSU J. Eng. Sci., vol. 27, no. 1, pp. 92–104, Mar. 2024, doi: 10.17780/ksujes.1358512.

ISNAD

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

JAMA

1.Temel T, Kılıçarslan M, Hoşcan Y. SDA: A NOVEL SKEWED-DEEP-ARCHITECTURE FOR VEHICLE MOTION DETECTION IN DRIVING VIDEOS. KSU J. Eng. Sci. 2024;27:92–104.

MLA

Temel, Tansu, et al. “SDA: A NOVEL SKEWED-DEEP-ARCHITECTURE FOR VEHICLE MOTION DETECTION IN DRIVING VIDEOS”. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, vol. 27, no. 1, Mar. 2024, pp. 92-104, doi:10.17780/ksujes.1358512.

Vancouver

1.Tansu Temel, Mehmet Kılıçarslan, Yaşar Hoşcan. SDA: A NOVEL SKEWED-DEEP-ARCHITECTURE FOR VEHICLE MOTION DETECTION IN DRIVING VIDEOS. KSU J. Eng. Sci. 2024 Mar. 1;27(1):92-104. doi:10.17780/ksujes.1358512

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