Integrasi YOLOv11 dan Intersection-Based Method Untuk Estimasi Karakteristik Parkir Berdasarkan Parking Lot Surveillance Video
Authors
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Muhammad Kamal Robbani
Universitas Pendidikan Indonesia
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Yudi Wibisono
Universitas Pendidikan Indonesia
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Eddy Prasetyo Nugroho
Universitas Pendidikan Indonesia
DOI:
https://doi.org/10.62712/juktisi.v4i2.502Keywords:
YOLOv11, Computer Vision, Object Detection, Classification, Parking ManagementAbstract
The rapid growth of vehicles without a corresponding increase in parking space availability has led to various issues such as traffic congestion, fuel waste, and excessive emissions. This study develops a computer vision-based parking analysis system using the YOLOv11 model to automatically detect vehicles in parking areas. The system integrates an intersection-based method and the BoT-SORT object tracking algorithm to classify parking spot availability. The classification results are then used to extract parking characteristic data. Video data were obtained from a publicly accessible livestream on YouTube in Kusatsu, Japan, and used for training and evaluating the model. The model achieved an mAP@50-95 of 0.926 under bright lighting conditions and 0.859 in low-light conditions. Additionally, estimation accuracy was evaluated using MAE and R² metrics, showing promising results, with MAE of 1.27 and R² of 0.989 during daytime, and MAE of 0.91 and R² of 0.91 at night.
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References
A. Ahdiat, “Ini Pertumbuhan Jumlah Mobil di Indonesia 10 Tahun Terakhir,” Databoks - Katadata. Accessed: Jul. 12, 2025. [Online]. Available: https://databoks.katadata.co.id/transportasi-logistik/sta tistik/f266a30f3f686f1/ini-pertumbuhan-jumlah-mobil-di-indonesia-10-tahun-terakhir
M. R. Arfianto, “Analisis Desain User Interface pada Aplikasi Pencari Parkir Mobil,” Desainpedia J. Urban Des. Lifestyle Behav., vol. 1, no. 1, 2022, doi: 10.36262/dpj.v1i1.589.
Z. Zhao and Y. Zhang, “A Comparative Study of Parking Occupancy Prediction Methods considering Parking Type and Parking Scale,” J. Adv. Transp., vol. 2020, 2020, doi: 10.1155/2020/5624586.
D. A. Yulmida, S. W. Mudjanarko, M. I. Setiawan, and A. D. Limantara, “Analisis Kinerja Parkir Sepanjang Jalan Walikota Mustajab Surabaya,” UKaRsT, vol. 1, no. 1, p. 11, 2018, doi: 10.30737/ukarst.v1i1.81.
B. Benjdira, A. Koubaa, W. Boulila, and A. Ammar, “Parking Analytics Framework using Deep Learning,” in Proceedings - 2022 2nd International Conference of Smart Systems and Emerging Technologies, SMARTTECH 2022, 2022. doi: 10.1109/SMARTTECH54121.2022.00051.
N. Jegham, C. Y. Koh, M. Abdelatti, and A. Hendawi, “YOLO Evolution: A Comprehensive Benchmark and Architectural Review of YOLOv12, YOLO11, and Their Previous Versions,” 2025, [Online]. Available: http://arxiv.org/abs/2411.00201
F. Galbusera and A. Cina, “Image annotation and curation in radiology: an overview for machine learning practitioners,” 2024. doi: 10.1186/s41747-023-00408-y.
Ultralytics, “Data Preprocessing Techniques for Annotated Computer Vision Data,” Ultralytics. Accessed: Jul. 15, 2025. [Online]. Available: https://docs.ultralytics.com/guides/preprocessing_annotated_data/
Ultralytics, “Ultralytics YOLO Hyperparameter Tuning Guide,” Ultralytics. Accessed: Jul. 15, 2025. [Online]. Available: https://docs.ultralytics.com/guides/hyperparameter-tuning/
F. Fritsch, “Deep Neural Networks for Object Detection in Satellite Imagery,” 2023.
A. Martynova et al., “Revising deep learning methods in parking lot occupancy detection,” pp. 1–22, 2023, [Online]. Available: http://arxiv.org/abs/2306.04288
N. Aharon, R. Orfaig, and B.-Z. Bobrovsky, “BoT-SORT: Robust Associations Multi-Pedestrian Tracking,” no. 2, 2022, [Online]. Available: http://arxiv.org/abs/2206.14651
A. Ultsch and J. Lötsch, “Euclidean distance-optimized data transformation for cluster analysis in biomedical data (EDOtrans),” BMC Bioinformatics, vol. 23, no. 1, 2022, doi: 10.1186/s12859-022-04769-w.
G. Nurinda Abdi and A. Nur Jannah, “EVALUASI KARAKTERISTIK DAN KEBUTUHAN RUANG PARKIR SEPEDA MOTOR (STUDI KASUS PT TIRTA INVESTAMA WONOSOBO),” Teknisia, vol. 27, no. 1, 2022, doi: 10.20885/teknisia.vol27.iss1.art6.
G. Pramono, N. M. Rangkuti, and M. Lubis, “Analisa Durasi dan Kapasitas Parkir Manhattan Times Square,” J. Civ. Eng. Build. Transp., vol. 3, no. 1, 2019, doi: 10.31289/jcebt.v3i1.2457.
J. Qi, J. Du, S. M. Siniscalchi, X. Ma, and C. H. Lee, “On Mean Absolute Error for Deep Neural Network Based Vector-to-Vector Regression,” IEEE Signal Process. Lett., vol. 27, 2020, doi: 10.1109/LSP.2020.3016837.
H. Pham, “A new criterion for model selection,” Mathematics, vol. 7, no. 12, 2019, doi: 10.3390/MATH7121215.
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Copyright (c) 2025 Muhammad Kamal Robbani, Yudi Wibisono, Eddy Prasetyo Nugroho
This work is licensed under a Creative Commons Attribution 4.0 International License.
