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All Journal Proceeding of the Electrical Engineering Computer Science and Informatics Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) MULTINETICS eProceedings of Engineering Journal of Computer Engineering: Progress, Application and Technology
Fussy Mentari Dirgantara
Telkom University
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering

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Pedestrian Detection System using YOLOv5 for Advanced Driver Assistance System (ADAS) Surya Michrandi Nasution; Fussy Mentari Dirgantara
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 3 (2023): Juni 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i3.4884

Abstract

The technology in transportation is continuously developing due to reaching the self-driving vehicle. The need of detecting the situation around vehicles is a must to prevent accidents. It is not only limited to the conventional vehicle in which accident commonly happens, but also to the autonomous vehicle. In this paper, we proposed a detection system for recognizing pedestrians using a camera and minicomputer. The approach of pedestrian detection is applied using object detection method (YOLOv5) which is based on the Convolutional Neural Network. The model that we proposed in this paper is trained using numerous epochs to find the optimum training configuration for detecting pedestrians. The lowest value of object and bounding box loss is found when it is trained using 2000 epochs, but it needs at least 3 hours to build the model. Meanwhile, the optimum model’s configuration is trained using 1000 epochs which has the biggest object (1.49 points) and moderate bounding box (1.5 points) loss reduction compared to the other number of epochs. This proposed system is implemented using Raspberry Pi4 and a monocular camera and it is only able to detect objects for 0.9 frames for each second. As further development, an advanced computing device is needed due to reach real-time pedestrian detection.
Co-Authors Adita Sukma Wardani Agus Virgono Arief Syaichu-Rohman Attallah Arelian Naufhal Casi Setianingsih Davy Rachmandiaz Hartoyo Lenni Yulianti Miftah Amirul Amin Mohammad Naufal Nabil Abdillah Muhammad Arham Irsyad Muhammad Emir Ghiffari Muhammad Nur Habibi Prabu Arie Pradana R Rogers Dwiputra Setiady Randy Erfa Saputra Reza Rendian Septiawan Surya Michrandi Nasution Syaichu-Rohman, Arief Umar Ali Ahmad Yudha Pratama Yulianti, Lenni