Multi-Object Detection in Open Spaces Using YOLOV8
Deteksi Multi Wajah pada Ruang Terbuka Menggunakan YOLOv8
DOI:
https://doi.org/10.21070/ups.8542Keywords:
Real Time, YOLOv8, CCTVAbstract
This research presents a facial recognition system using YOLOv8, known for its speed and accuracy in real-time detection, especially in outdoor settings. The dataset combines CCTV footage from SMK YPM 8 Sidoarjo and the WIDER Face Dataset, annotated and augmented via Roboflow. The process involves image preprocessing, bounding box labeling, and training with pretrained YOLOv8n weights on Google Colab. Evaluation focuses on detection precision, inference time, and adaptability to lighting and environmental changes. Results show that YOLOv8 performs well in dynamic conditions but may struggle with strong lighting or non-frontal faces. This system is promising for security and surveillance, with future improvements aimed at better augmentation and parameter tuning.
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References
N. Hidayat, S. Wahyudi, and A. A. Diaz, “Individual Recognition Through Face Identification Using the You Only Look Once (YOLOv5) Method,” E-prosiding Semin. Nas. Mat. Geom. Stat. Dan Komputasi, pp. 85–98, 2022, [Online]. Available: https://magestic.unej.ac.id/
R. Gelar Guntara, “Pemanfaatan Google Colab Untuk Aplikasi Pendeteksian Masker Wajah Menggunakan Algoritma Deep Learning YOLOv7,” J. Teknol. Dan Sist. Inf. Bisnis, vol. 5, no. 1, pp. 55–60, 2023, doi: 10.47233/jteksis.v5i1.750.
Y. Yanto, F. Aziz, and I. Irmawati, “Yolo-V8 Peningkatan Algoritma Untuk Deteksi Pemakaian Masker Wajah,” JATI (Jurnal Mhs. Tek. Inform., vol. 7, no. 3, pp. 1437–1444, 2023, doi: 10.36040/jati.v7i3.7047.
R. Illmawati and Hustinawati, “YOLO v5 untuk Deteksi Nomor Kendaraan di DKI Jakarta,” J. Inf. Syst. Res., vol. 10, no. 1, pp. 32–43, 2022.
R. N. Zakaria, R. Wulanningrum, and A. B. Setiawan, “Penerapan Segmentasi Wajah MenggunakanYOLOv8 Untuk Presensi Mata Kuliah,” Agustus, vol. 8, pp. 2549–7952, 2024.
L. Satya, M. R. D. Septian, M. W. Sarjono, M. Cahyanti, and E. R. Swedia, “Sistem Pendeteksi Plat Nomor Polisi Kendaraan Dengan Arsitektur Yolov8,” Sebatik, vol. 27, no. 2, pp. 753–761, 2023, doi: 10.46984/sebatik.v27i2.2374.
A. A. Rasjid, B. Rahmat, and A. N. Sihananto, “Implementasi YOLOv8 Pada Robot Deteksi Objek,” J. Technol. Syst. Inf., vol. 1, no. 3, p. 9, 2024, doi: 10.47134/jtsi.v1i3.2969.
M. Abdurahman Kamil and Y. Mitha Djaksana, “Perbandingan Algoritma Convolutional Neural Network (Cnn) Dan Algoritma You Only Look Once (Yolo) Untuk Deteksi Wajah Comparison of Convolutional Neural Network (Cnn) Algorithm and You Only Look Once (Yolo) Algorithm for Facial Detection,” Jiic J. Intelek Insa. Cendikia, vol. 1, no. 9, pp. 4866–4876, 2024, [Online]. Available: https://jicnusantara.com/index.php/jiic
D. J. Marcelleno and M. P. K. Putra, “Performance Evaluation of Yolov8 in Real-Time Vehicle Detection in Various Environmental Conditions,” J. Tek. Inform., vol. 6, no. 1, pp. 269–279, 2025, doi: 10.52436/1.jutif.2025.6.1.3916.
N. Akbar and A. Rahman, “Pengaruh Image Size pada Penghitung Jumlah Orang Dalam Ruangan Menggunakan Metode YOLOv8,” J. Algoritm., vol. 5, no. 2, pp. 176–187, 2025, doi: 10.35957/algoritme.v5i2.9035.
B. C. L. Adiatma, E. Utami, and A. D. Hartanto, “Pengenalan Ekspresi Wajah Menggunakan Deep Convolutional Neural Network,” Explore, vol. 11, no. 2, p. 75, 2021, doi: 10.35200/explore.v11i2.478.
D. P. Pamungkas, I. Yanuartanti, and D. Erwanto, “Sistem Pendeteksi Identitas Dengan Pengenalan Wajah Menggunakan Yolo,” no. 38, 2024.
M. B. Laili et al., “Implementasi Sistem Pendeteksi Buku dengan YOLOv8,” vol. 8, pp. 35–43.
R. Dijaya, I. Anshory, R. A. Sukmono, and A. R. Fajaresta, “Facial Fatigue Detection in High-Risk Occupational Environments: Leveraging YOLOv4 for Enhanced Worker Safety,” 2023 1st Int. Conf. Adv. Eng. Technol. ICONNIC 2023 - Proceeding, pp. 356–361, 2023, doi: 10.1109/ICONNIC59854.2023.10468037.
A. Ramdan et al., “Implementasi Deteksi Objek Real-Time Sebagai Media Edukasi dengan Algoritma YOLOv8 pada Objek Sampah sampah kota , limbah makanan , limbah ( Sistem Informasi Pengelolaan Sampah Dengan memanfaatkan Artificial Intelligence , kita dapat menciptakan menurut B,” J. SAINTEKOM Sains, Teknol. KOmputer, dan Manaj., vol. 14, no. 2, pp. 142–153, 2024, [Online]. Available: https://ojs.stmikplk.ac.id/index.php/saintekom/article/view/638
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