Haerunnisya Makmur Wulandari, Fajar B Muhammad, Andi Baso Kaswar, Dyah Darma Andayani, Fhatiah Adiba, Abdul Wahid, Satria Gunawan Zain
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 Fl-score of 86.2% with a confidence threshold of 0.5. © (2025), (Taiwan Association of Engineering and Technology Innovation). All rights reserved.
Department of Computer Engineering, State University of Makassar, Makassar, Indonesia
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