Real-Time Object Detection Performance of YOLOv8 Models for Self-Driving Cars in a Mixed Traffic Environment-Reference-Cited by-同舟云学术

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Real-Time Object Detection Performance of YOLOv8 Models for Self-Driving Cars in a Mixed Traffic Environment

Published:2023-08-02 Issue: Volume: Page:
ISSN:
Container-title:2023 2nd International Conference on Computer System, Information Technology, and Electrical Engineering (COSITE)
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Author:

Afdhal Afdhal¹,Saddami Khairun²,Sugiarto Sugiarto³,Fuadi Zahrul⁴,Nasaruddin Nasaruddin²

Affiliation:

1. Universitas Syiah Kuala,Doctoral Program, School of Engineering,Indonesia

2. Universitas Syiah Kuala,Department of Electrical and Computer Engineering,Indonesia

3. Universitas Syiah Kuala,Department of Civil Engineering,Indonesia

4. Universitas Syiah Kuala,Department of Mechanical dan Industrial Engineering,Indonesia

Funder

Universitas Syiah Kuala

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10249207/10249013/10249521.pdf?arnumber=10249521

Reference15 articles.

1. YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors;wang;arXiv 2207 02696,2022

2. Stack-YOLO: A Friendly-Hardware Real-Time Object Detection Algorithm

3. SF-YOLOv5: A Lightweight Small Object Detection Algorithm Based on Improved Feature Fusion Mode

4. Yolov6: A single-stage object detection framework for industrial applications;li;arXiv preprint arXiv 2209 02976,2022

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