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Sri Arum Kinanti
Jurusan Sistem Komputer Fakultas Ilmu Komputer Universitas Sriwijaya
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Industrial & Manufacturing Engineering Library & Information Science

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Akurasi Pengujian Model Hasil Training menggunakan YOLOv4 untuk Pengenalan Kendaraan di Jalan Raya Ahmad Fali Oklilas; sukemi; Dinda Dwinta; Ghinadhia Shofi; Nanda Putri Mariza; Sri Arum Kinanti; Yulia Amanda Sari
JUPITER (Jurnal Penelitian Ilmu dan Teknologi Komputer) Vol 15 No 1d (2023): Jupiter Edisi April 2023
Publisher : Teknik Komputer Politeknik Negeri Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281./6537/15.jupiter.2023.04

Abstract

Traffic congestion is currently the main problem that occurs in big cities in Indonesia.Traffic flow analysis is an important basis for urban planning. Management of IntelligentTransportation System (ITS) has become a necessity today to manage heavy traffic problems.Intelligent transportation systems using computer vision techniques are increasingly attractingattention for traffic density detection. This research uses the You Only Look Once (YOLO version4 object detection method for vehicle classification and detection to obtain an optimal model.Testing the YOLOv4 model results in a mean average precision (mAP) of 80.12%. In video testingto detect motorcycles and cars, the total vehicle accuracy is 70.6% and the vehicle confidencelevel is 78.7%.
Co-Authors Ahmad Fali Oklilas Dinda Dwinta Ghinadhia Shofi Nanda Putri Mariza sukemi Yulia Amanda Sari