Motorcycle Parking Violation Detection Using YOLOv12 Segmentation and ROI-Guided Orientation Analysis

Muh Fajrin Bakri; Shahnaz Tasha Kurnia; Muhammad Fajar B; Andi Baso Kaswar; Dyah Darma Andayani; Fhatiah Adiba; Sanatang; Syahrul

doi:10.46604/peti.2026.16020

Authors

Muh Fajrin Bakri Department of Computer Engineering, State University of Makassar, Makassar, Indonesia
Shahnaz Tasha Kurnia Department of Computer Engineering, State University of Makassar, Makassar, Indonesia
Muhammad Fajar B Department of Computer Engineering, State University of Makassar, Makassar, Indonesia
Andi Baso Kaswar Department of Computer Engineering, State University of Makassar, Makassar, Indonesia
Dyah Darma Andayani Department of Computer Engineering, State University of Makassar, Makassar, Indonesia
Fhatiah Adiba Department of Computer Engineering, State University of Makassar, Makassar, Indonesia
Sanatang Department of Computer Engineering, State University of Makassar, Makassar, Indonesia
Syahrul Department of Computer Engineering, State University of Makassar, Makassar, Indonesia

DOI:

https://doi.org/10.46604/peti.2026.16020

Keywords:

parking violation detection, YOLOv12, image segmentation, orientation analysis, region of interest

Abstract

This study aims to improve the accuracy of legal and illegal motorcycle parking classification by proposing a computer vision-based detection system using YOLOv12 segmentation and region-of-interest (ROI)-guided orientation analysis. The proposed system integrates object segmentation, ROI mapping, and computation of the angular deviation between vehicle orientation and parking guides. To determine the optimal orientation tolerance, experiments are conducted using multiple angular thresholds under identical datasets and testing scenarios. The system is evaluated across three locations using confusion-matrix-based performance metrics. The results show that a 30° tolerance yields the best performance, achieving an average accuracy of 87.29%, precision of 93.14%, recall of 92.23%, and F1-score of 92.68%. These findings indicate that ROI-guided orientation analysis enhances the reliability of motorcycle parking violation detection.

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