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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Masyarakat Informatika MATEMATIKA Journal of Informatics and Technology TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Abdimas Jurnal Ilmiah Kursor JUITA : Jurnal Informatika Scientific Journal of Informatics Jurnal IPTEK Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika E-Dimas: Jurnal Pengabdian kepada Masyarakat Sinkron : Jurnal dan Penelitian Teknik Informatika International Journal of Advances in Artificial Intelligence and Machine Learning
Helmie Arif Wibawa
The Informatics Departement, Faculty Of Mathemathics And Sciences, Diponegoro University
Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Social Sciences

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Journal : Bulletin of Electrical Engineering and Informatics

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Improved car detection performance on highways based on YOLOv8 Sutikno, Sutikno; Sugiharto, Aris; Kusumaningrum, Retno; Wibawa, Helmie Arif
Bulletin of Electrical Engineering and Informatics Vol 13, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i5.8031

Abstract

Car detection on the road through computer vision is crucial for improving safety, as it plays an essential role in spotting nearby vehicles and preventing fatal accidents. Additionally, car detection significantly contributes to the advancement of autonomous vehicles. Previous explorations of car detection using YOLOv5 have revealed weaknesses regarding its resulting mean average precision (mAP). This scenario led to the development of a more advanced version of you only look once (YOLO), namely YOLOv8. Consequently, this study aimed to adopt YOLOv8 for automatic car detection on the road. YOLOv8 is proven to perform better than the previous version. A dataset comprising video frame images was captured on the highway in Semarang, Indonesia. The experiment results indicated that the proposed approach achieved impressive precision, recall, and mAP values, reaching 94.1%, 98.2%, and 98.8%, respectively. The proposed approach enhanced mAP and training time when compared with YOLOv5. Therefore, it was concluded that the proposed method was better suited for real-time car detection.
Co-Authors Adi Wibowo Anneta Shifa Ichwani Aris Sugiharto Arkan, Tsaqif Muhammad Defa Hanifta Putra, Defa Hanifta Dhemma Ratanajaya Dikki Mahasati Dwi Kusrianto Putro, Dwi Kusrianto Fouziya, MD Frediansah Frediansah Herwindyo, Sony Ichwani, Anneta Shifa Indra Waspada Kushartantya Kushartantya Mochammad Hosam Muhamad Subhan Efendi Panji Wisnu Wirawan Prathina , Paila Prima Yusuf Budiarto Priyo Sidik Sasongko Ragil Saputra Ratanajaya, Dhemma Retno Kusumaningrum RISMIYATI RISMIYATI Rithika, Madas Satriyo Adhy Sekar Ratnaningtyas Sruthi, Avula Sukmawati Nur Endah Sukmawati Nur Endah Sukmawati Nur Endah Sushma, Parsha Sutikno Sutikno Sutikno Sutikno Sutikno Sutikno Syaakir Ni’am Taufan Dwi Prayogo