Smart Traffic untuk Menghitung Volume Kendaraan dan Klasifikasi Kondisi Lalu Lintas Menggunakan Model YOLOv7
DOI:
https://doi.org/10.35746/jtim.v7i2.667Kata Kunci:
Klasifikasi, Volume Kendaraan, You Only Look Once (YOLO) v7Abstrak
One of the most complex challenges in urban management, particularly in developing countries, is traffic control. Traffic congestion has become a global issue, significantly affecting mobility, economic productivity, and quality of life. To address this problem, smart traffic systems are increasingly being adopted as adaptive and efficient solutions. This study aims to implement the You Only Look Once version 7 (YOLOv7) object detection model within a smart traffic system to calculate vehicle volume and monitor traffic conditions in real time. YOLOv7 is chosen for its high object detection accuracy, even in dynamic and complex environments where objects are fast-moving or overlapping in dense backgrounds. The methodology involves processing a 2-minute-30-second CCTV video recording taken from a street in New York City. Vehicle detection is conducted by applying bounding boxes over specific areas within the video frames, which serve as virtual counters for vehicles passing through. The experimental results demonstrate that the system effectively counts vehicles per second and identifies traffic conditions, which in this case remained smooth throughout the observation period. These findings highlight the potential of implementing YOLOv7 in smart traffic systems to support data-driven, automated, and real-time traffic management.
Unduhan
Referensi
F. Arif, A. Nurkholis, S. Laia, and P. Rosyani, “Deteksi Kendaraan Dengan Metode YOLO,” J. Artif. Intel. dan Sist. Penunjang Keputusan, vol. 2, no. 1, 2023.
M. Sauqi, “Deteksi Kendaraan Menggunakan Algoritma You Only Look Once (YOLO) V3,” Univ. Islam Indones., 2022, https://dspace.uii.ac.id/handle/123456789/38956.
A. A. B, A. Amin, and M. W. Kasrani, “PENERAPAN METODE YOLO OBJECT DETECTION V1 TERHADAP PROSES PENDETEKSIAN JENIS KENDARAAN DI PARKIRAN,” J. Tek. Elektro Uniba (JTE UNIBA), vol. 6, no. 1, 2021, doi: https://doi.org/10.36277/jteuniba.v6i1.130.
F. Ramadhani, A. Satria, and S. Dewi, “Identifikasi Kendaraan Bermotor pada Dashcam Mobil Menggunakan Algoritma YOLO,” Hello World J. Ilmu Komput., vol. 2, no. 4, 2024, doi: https://doi.org/10.56211/helloworld.v2i4.466.
A. Amwin, “Deteksi Dan Klasifikasi Kendaraan Berbasis Algoritma You Only Look Once (YOLO),” Univ. Islam Indones., 2021, https://dspace.uii.ac.id/handle/123456789/34154.
P. Y. Putra, A. S. Arifianto, Z. E. Fitri, and T. D. Puspitasari, “Deteksi Kendaraan Truk pada Video Menggunakan Metode Tiny-YOLO v4,” J. Inform. Polinema, vol. 9, no. 2, 2023, doi: https://doi.org/10.33795/jip.v9i2.1243.
A. F. Oklilas, S. Sukemi, and R. Apriliyanto, “MODEL YOLO VERSI 4 PADA PENGENALAN KENDARAAN DI JALAN RAYA KOTA PALEMBANG,” Transm. J. Ilm. Tek. Elektro, vol. 25, no. 3, 2023, doi: https://doi.org/10.14710/transmisi.25.3.136-139.
H. J. Pramana, P. Purwanto, and P. Pujiono, “Pengenalan Objek Kendaraan Bermotor Berbasis Framework YOLO Dengan Metode Convolutional Neural Network,” J. VOI (Voice Informatics), vol. 12, no. 1, 2023.
F. Jupiter, E. S. Negara, Y. N. Kunang, and M. I. Herdiansyah, “Implementasi Algoritma CNN dan YOLO untuk Mendeteksi Jenis Kendaraan pada Jalan Raya,” Explor. J. Sist. Inf. dan Telemat., vol. 14, no. 2, 2023, doi: https://doi.org/10.36448/jsit.v14i2.3259.
M. R. Ardiansyah, Y. Supit, and M. S. Said, “SISTEM VISI KOMPUTER UNTUK KALKULASI KEPADATAN KENDARAAN MENGGUNAKAN ALGORITMA YOLO,” Simtek J. Sist. Inf. dan Tek. Komput., vol. 7, no. 1, 2022, doi: https://doi.org/10.51876/simtek.v7i1.123.
E. Ektrada, L. Hakim, and S. P. Kristanto, “Sistem Tracking dan Counting Kendaraan Berbasis YOLO untuk Pemetaan Slot Parkir Kendaraan,” Softw. Dev. Digit. Bus. Intell. Comput. Eng., vol. 1, no. 02, 2023, doi: https://doi.org/10.57203/session.v1i02.2023.55-60.
N. J. Hayati, D. Singasatia, and M. R. Muttaqin, “Object Tracking Menggunakan Algoritma You Only Look Once (YOLO)v8 untuk Menghitung Kendaraan,” Komputa J. Ilm. Komput. dan Inform., vol. 12, no. 2, 2023, doi: https://doi.org/10.34010/komputa.v12i2.10654.
M. I. Hermawan, I. I. Tritoasmoro, and Ibrahim Nur, “PENGATURAN LAMPU LALU LINTAS BERDASARKAN KEPADATAN KENDARAAN MENGGUNAKAN METODE YOLO,” e-Proceeding Eng., vol. 8, no. 1, 2021.
A. Faqih, K. Mutmainnah, and M. Afifah R, “Seperation Deteksi Kendaraan Pada Citra Digital Dengan Menggunakan Algoritma YOLO (You Only Look Once),” J. Tek. Inform. dan Elektro, vol. 3, no. 2, 2021, doi: https://doi.org/10.55542/jurtie.v3i2.426.
A. Bathija and G. Sharma, “Visual Object Detection and Tracking using YOLO and SORT,” Int. J. Eng. Res. Technol., vol. 8, no. 11, pp. 705–708, 2019, https://www.ijert.org/research/visual-object-detection-and-tracking-using-yolo-and-sort-IJERTV8IS110343.pdf
M. G. Ragab et al., “A Comprehensive Systematic Review of YOLO for Medical Object Detection (2018 to 2023),” IEEE Access, vol. 12, 2024, doi: https://doi.org/10.1109/ACCESS.2024.3386826.
N. Bhavana, M. M. Kodabagi, B. M. Kumar, P. Ajay, N. Muthukumaran, and A. Ahilan, “POT-YOLO: Real-Time Road Potholes Detection Using Edge Segmentation-Based Yolo V8 Network,” IEEE Sens. J., vol. 24, no. 5, 2024, doi: https://doi.org/10.1109/JSEN.2024.3399008.
F. Rachmawati and D. Widhyaestoeti, “Deteksi Jumlah Kendaraan di Jalur SSA Kota Bogor Menggunakan Algoritma Deep Learning YOLO,” Pros. LPPM UIKA Bogor, 2020, https://pkm.uika-bogor.ac.id/index.php/prosiding/article/view/657
G. Boesch, “YOLOv7: A Powerful Object Detection Algorithm (2024 Guide),” viso.ai. https://viso.ai/deep-learning/yolov7-guide/#:~:text=As previously shown in the,speed and higher detection accuracy
Unduhan
Diterbitkan
Terbitan
Bagian
Lisensi
Hak Cipta (c) 2025 Kurniadin Abd Latif, Putri Tanisa Utami, Apriani Apriani, Fatimatuzzahra Fatimatuzzahra, Ria Rismayati
Artikel ini berlisensiCreative Commons Attribution-ShareAlike 4.0 International License.
