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All Journal Jurnal Ilmu Multidisplin
Muhammad Syafiq Reynara
Faculty of Management and Business, Institut Transportasi dan Logistik Trisakti, Jakarta, Indonesia
Agriculture, Biological Sciences & Forestry Computer Science & IT Economics, Econometrics & Finance Education Law, Crime, Criminology & Criminal Justice Public Health Social Sciences

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Revolutionizing Road Safety: Deep Learning in Vehicle Detection on Rs Fatmawati Road Muhammad Syafiq Reynara; Oktaria Dwi Yanti; Abdullah Ade Suryobuwono; Prima Widiyanto
Jurnal Ilmu Multidisiplin Vol. 3 No. 4 (2025): Jurnal Ilmu Multidisplin (Januari-Maret 2025)
Publisher : Green Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38035/jim.v3i4.2106

Abstract

This study evaluates the YOLOv8 deep learning model for vehicle detection on Jalan RS Fatmawati, Jakarta, using publicly available CCTV footage. Extensive pre-processing and data augmentation enhance model robustness, with performance assessed through metrics like mAP, precision, recall, and F1-score. Results indicate YOLOv8's high accuracy and reliability in real-time vehicle detection across various weather and lighting conditions, offering significant implications for urban planning and traffic management. The research also compares YOLOv8 with previous models (YOLOv4 and YOLOv5), revealing superior performance under specific conditions but challenges in extreme environments. These findings underline the model's practical utility and identify areas for future research.
Co-Authors Abdullah Ade Suryobuwono Oktaria Dwi Yanti Prima Widiyanto