PERFORMANCE EVALUATION OF YOLOV8 IN REAL-TIME VEHICLE DETECTION IN VARIOUS ENVIRONMENTAL CONDITIONS
Abstract
This research focuses on assessing and developing a real-time detection system using the YOLOv8 algorithm. Accurate and fast vehicle detection is a big challenge in modern traffic management, especially in various environmental conditions such as bad weather, low lighting, and high traffic density. The aim of this study was to evaluate the performance of YOLOv8 under these conditions and identify potential improvements. The dataset used consists of 16,990 vehicle images with various variations and environmental conditions. After being trained, the model is evaluated using metrics such as precision, recall, and F1-score, as well as Intersection over Union (IoU) with a threshold of 0.8 on IoU. The results show that YOLOv8 is superior with a fairly high detection accuracy of 78%, with precision of 82% and recall above 90%, and is able to detect vehicles in real-time conditions. However, the challenge of detecting small objects or irregularly shaped vehicles such as tractors still needs to be optimized. This research also compared the performance of YOLOv8 with the SSD (Single Shot Detector) algorithm, where YOLOv8 was proven to be superior in terms of accuracy, precision, recall and F1-score. The research results obtained provide valuable insights for the development of traffic management systems based on deep learning technology. The main contribution of this research is to provide a more efficient and effective vehicle detection solution, which can be applied in modern traffic management systems. Thus, it is hoped that the results of this research can increase the efficiency of traffic management and have a positive impact on the development of intelligent transportation systems in the future.
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T. Sutisna, A. Rachmat Raharja, E. Hariyadi, and V. Hafizh Cahaya Putra, “Penggunaan Computer Vision untuk Menghitung Jumlah Kendaraan dengan Menggunakan Metode SSD (Single Shoot Detector),” Innov. J. Soc. Sci. Res., vol. 4, pp. 6060–6067, 2024, [Online]. Available: https://j-innovative.org/index.php/Innovative/article/view/10071/6958
J. S. W. Hutauruk, T. Matulatan, and N. Hayaty, “Deteksi Kendaraan secara Real Time menggunakan Metode YOLO Berbasis Android,” J. Sustain. J. Has. Penelit. dan Ind. Terap., vol. 9, no. 1, pp. 8–14, 2020, doi: 10.31629/sustainable.v9i1.1401.
R. G. Fajri, I. Santoso, and Y. A. Adi Soetrisno, “Perancangan Program Pendeteksi Dan Pengklasifikasi Jenis Kendaraan Dengan Metode Convolutional Neural Network (Cnn) Deep Learning,” Transient J. Ilm. Tek. Elektro, vol. 9, no. 1, pp. 97–106, 2020, doi: 10.14710/transient.v9i1.97-106.
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, pp. 91–99, 2023, doi: 10.34010/komputa.v12i2.10654.
L. You, Y. Chen, C. Xiao, C. Sun, and R. Li, “Multi-Object Vehicle Detection and Tracking Algorithm Based on Improved YOLOv8 and ByteTrack,” Electron., vol. 13, no. 15, 2024, doi: 10.3390/electronics13153033.
A. Sundaresan Geetha, M. A. R. Alif, M. Hussain, and P. Allen, “Comparative Analysis of YOLOv8 and YOLOv10 in Vehicle Detection: Performance Metrics and Model Efficacy,” Vehicles, vol. 6, no. 3, pp. 1364–1382, 2024, doi: 10.3390/vehicles6030065.
A. Rezky, A. Bagir, D. Pamerean, and F. Makhrus, “Deteksi Kecelakaan Lalu Lintas Otomatis Pada Rekaman CCTV Indonesia Menggunakan Deep Learning,” Bul. Pagelaran Mhs. Nas. Bid. Teknol. Inf. dan Komun., vol. 1, no. 1, pp. 1–5, 2023, [Online]. Available: https://buletingemastik.id/index.php/bg/article/view/2/1
I. Irawanto, A. Sunyoto, and K. Kusnawi, “Peningkatan Akurasi Deteksi Kendaraan Menggunakan Kombinasi Haar Cascade Classifier dan Convolutional Neural Networks (CNN),” J. Electr. Eng. Comput., vol. 6, no. 1, pp. 47–57, 2024, doi: 10.33650/jeecom.v6i1.8242.
A. Meirza and N. R. Puteri, “Implementasi Metode YOLOV5 dan Tesseract OCR untuk Deteksi Plat Nomor Kendaraan,” vol. 9, pp. 424–435, 2024, [Online]. Available: https://journal.unusida.ac.id/index.php/jik/article/view/1288/887
M. G. A. Binuri, T. Haryanti, and M. A. Haq, “Penerapan Algoritma YOLO v7 Sebagai Deteksi Kecelakaan Kendaraan Pada Lalu Lintas,” Comput. Insight J. Comput. Sci., vol. 4, no. 2, pp. 7–14, 2022, [Online]. Available: https://journal.um-surabaya.ac.id/CI/article/view/22524/7607
A. F. Oklilas, “MODEL YOLO VERSI 4 PADA PENGENALAN KENDARAAN DI JALAN RAYA KOTA PALEMBANG,” no. 3, pp. 136–139, 2023, [Online]. Available: https://ejournal.undip.ac.id/index.php/transmisi/article/download/52732/pdf
amwin Aldhiyatika, “Algoritma You Only Look Once (Yolo) Tugas Akhir,” pp. 1–60, 2021, [Online]. Available: https://dspace.uii.ac.id/bitstream/handle/123456789/34154/17523176 Aldhiyatika Amwin.pdf?sequence=1&isAllowed=y
A. F. Hidayah et al., “MENGGUNAKAN ALGORITMA COUNTER DALAM,” vol. 11, no. 2, 2024, [Online]. Available: https://e-jurnal.lppmunsera.org/index.php/PROSISKO/article/view/8918/3220
R. G. Wijanarko, A. I. Pradana, and D. Hartanti, “IMPLEMENTASI DETEKSI DRONE MENGGUNAKAN YOLO ( You Only Look Once ),” vol. 14, no. 2, pp. 437–442, 2024, [Online]. Available: https://ejurnal.umri.ac.id/index.php/JIK/article/view/7374/3067
A. P. H. Telaumbanua, T. P. Larosa, P. D. Pratama, R. H. Fauza, and A. M. Husein, “Vehicle Detection and Identification Using Computer Vision Technology with the Utilization of the YOLOv8 Deep Learning Method,” Sinkron, vol. 8, no. 4, pp. 2150–2157, 2023, doi: 10.33395/sinkron.v8i4.12787.
M. IKBAL and R. A. Saputra, “Pengenalan Rambu Lalu Lintas Menggunakan Metode Yolov8,” JIKA (Jurnal Inform., vol. 8, no. 2, p. 204, 2024, doi: 10.31000/jika.v8i2.10609.
Y. Zhang, Z. Guo, J. Wu, Y. Tian, H. Tang, and X. Guo, “Real-Time Vehicle Detection Based on Improved YOLO v5,” Sustain., vol. 14, no. 19, 2022, doi: 10.3390/su141912274.
N. H. Harani, C. Prianto, and M. Hasanah, “Deteksi Objek Dan Pengenalan Karakter Plat Nomor Kendaraan Indonesia Menggunakan Metode Convolutional Neural Network (CNN) Berbasis Python,” J. Tek. Inform., vol. 11, no. 3, pp. 47–53, 2019, [Online]. Available: https://ejurnal.ulbi.ac.id/index.php/informatika/article/view/658
R. Wulanningrum and A. Sanjaya, “Implementasi YOLO Dalam Deteksi Jumlah Kendaraan,” vol. 8, pp. 1274–1281, 2024, [Online]. Available: https://proceeding.unpkediri.ac.id/index.php/inotek/article/view/5064/3498
M. A. Fahrezi et al., “Implementasi YOLOv8 Dalam Penghitung Masuk Dan Keluar Manusia Pada Gedung,” vol. 11, no. 3, 2024, [Online]. Available: https://jurnal.mdp.ac.id/index.php/jatisi/article/view/9050/2055
A. F. Oklilas, D. Dwinta, G. Shofi, N. P. Mariza, and ..., “Akurasi Pengujian Model Hasil Training menggunakan YOLOv4 untuk Pengenalan Kendaraan di Jalan Raya,” … J. Penelit. Ilmu …, pp. 799–806, 2023, [Online]. Available: https://jurnal.polsri.ac.id/index.php/jupiter/article/view/6537%0Ahttps://jurnal.polsri.ac.id/index.php/jupiter/article/download/6537/2528
M. Sauqi, “Deteksi Kendaraan Menggunakan Algoritma You Only Look Once (YOLO) V3,” Univ. Islam Indones., pp. 5–8, 2022, [Online]. Available: https://dspace.uii.ac.id/bitstream/handle/123456789/38956/17523142.pdf?sequence=1&isAllowed=y
D. Iskandar Mulyana and M. A. Rofik, “Implementasi Deteksi Real Time Klasifikasi Jenis Kendaraan Di Indonesia Menggunakan Metode YOLOV5,” J. Pendidik. Tambusai, vol. 6, no. 3, pp. 13971–13982, 2022, doi: 10.31004/jptam.v6i3.4825.
V. R. Pratiwi and N. Rijati, “Classification Vehicle Tire Quality using Convolutional Neural Networks,” J. Appl. Informatics Comput., vol. 8, no. 1, pp. 213–220, 2024, doi: 10.30871/jaic.v8i1.8074.
I. W. Adi, A. Wiguna, R. R. Huizen, and G. A. Pradipta, “Model Deteksi Objek Menggunakan Yolov5 untuk Pengendalian Pengaturan Lalu Lintas,” pp. 840–844, 2024, [Online]. Available: https://spinter.stikom-bali.ac.id/index.php/spinter/article/view/244/209
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.
A. Imran, “Deteksi Pelanggaran Pada Bahu Jalan Tol Dengan Intelligent Transportation System Menggunakan Algoritma Yolov5 Violation Detection On The Roadside Of The Toll Roads With Intelligent Transportation System Using Yolov5 Algorithm,” vol. 10, no. 5, p. 4498, 2023, [Online]. Available: https://openlibrarypublications.telkomuniversity.ac.id/index.php/engineering/article/view/17957/17589
Hendri Candra Mayana and Desmarita Leni, “Deteksi Kerusakan Ban Mobil Menggunakan Convolutional Neural Network dengan Arsitektur ResNet-34,” J. Surya Tek., vol. 10, no. 2, pp. 842–851, 2023, doi: 10.37859/jst.v10i2.6336.
L. Rahma, H. Syaputra, A. H. Mirza, and S. D. Purnamasari, “Objek Deteksi Makanan Khas Palembang Menggunakan Algoritma YOLO (You Only Look Once),” J. Nas. Ilmu Komput., vol. 2, no. 3, pp. 213–232, 2021, doi: 10.47747/jurnalnik.v2i3.534.
R. M. Taufiq, Sunanto, Y. Rizki, and M. R. A. Pratama, “Simulasi Deteksi Golongan Kendaraan pada Gerbang Tol Menggunakan YOLOv4,” J. CoSciTech (Computer Sci. Inf. Technol., vol. 3, no. 2, pp. 199–206, 2022, doi: 10.37859/coscitech.v3i2.3928.
Wahyono, M. E. Wibowo, A. Ashari, and M. P. K. Putra, “Improvement of Deep Learning-based Human Detection using Dynamic Thresholding for Intelligent Surveillance System,” Int. J. Adv. Comput. Sci. Appl., vol. 12, no. 10, pp. 472–477, 2021, doi: 10.14569/IJACSA.2021.0121053.
M. Dio Riza Pratama, B. Priyatna, S. S. Hilabi, and A. L. Hananto, “Deteksi Objek Kecelakaan Pada Kendaraan Roda Empat Menggunakan Algoritma YOLOv5,” J. Ilm. Sist. Informas, vol. 12, no. 2, pp. 15–26, 2022, [Online]. Available: https://journal.unipdu.ac.id/index.php/teknologi/article/view/3260/1523.
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