Motorcycle Parking Violation Detection System Using YOLOv7 with Region of Interest Mapping and Object Area Calculation

Haerunnisya Makmur; Wulandari; Muhammad Fajar B; Andi Baso Kaswar; Dyah Darma Andayani; Fhatiah Adiba; Abdul Wahid; Satria Gunawan Zain

doi:10.46604/aiti.2024.14075

Authors

Haerunnisya Makmur Department of Computer Engineering, State University of Makassar, Makassar, Indonesia
Wulandari 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
Abdul Wahid Department of Computer Engineering, State University of Makassar, Makassar, Indonesia
Satria Gunawan Zain Department of Computer Engineering, State University of Makassar, Makassar, Indonesia

DOI:

https://doi.org/10.46604/aiti.2024.14075

Keywords:

computer vision, parking violation, region of interest, motorcycle, YOLOv7

Abstract

The large number of motorcycle users has created challenges, particularly related to parking violations, which can lead to traffic congestion, hinder emergency access, disrupt pedestrian pathways, and inconvenience other users. Therefore, this study aims to detect motorcycle parking violations in unsupervised restricted areas using YOLOv7 to classify non-parking, parking, and personal objects. The best model is achieved at the 28th epoch with an mAP value of 0.953 at the 0.5 threshold. Parking restriction areas are defined using a Region of Interest (ROI), where violations depend on the parking object’s detected coverage within the ROI exceeding 50%. By employing an area calculation method, the results show better performance compared to methods without area calculation, achieving a recall of 89.7%, precision of 82.6%, and F1-score of 86.2% with a confidence threshold of 0.5.

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