Pedestrian Detection System using YOLOv5 for Advanced Driver Assistance System (ADAS)
Dublin Core
Title
Pedestrian Detection System using YOLOv5 for Advanced Driver Assistance System (ADAS)
Subject
pedestrian detection system; ADAS; intelligent transportation system; object detection; YOLOv5
Description
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
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
Creator
Surya Michrandi Nasution, Fussy Mentari Dirgantara
Source
http://jurnal.iaii.or.id
Publisher
Professional Organization Ikatan Ahli Informatika Indonesia (IAII)/Indonesian Informatics Experts Association
Date
June 2023
Contributor
Sri Wahyuni
Rights
ISSN Media Electronic: 2580-0760
Format
PDF
Language
English
Type
Text
Files
Collection
Citation
Surya Michrandi Nasution, Fussy Mentari Dirgantara, “Pedestrian Detection System using YOLOv5 for Advanced Driver Assistance System (ADAS),” Repository Horizon University Indonesia, accessed January 11, 2026, https://repository.horizon.ac.id/items/show/9958.