Car Detector by Brand and Type Using the YOLO Algorithm


Al Riansyah A Haidar Mirza


The government system in an area or city has regulations in regulating the activities of its residents, especially those related to driving on the highway. Traffic violations often occur on urban roads and highways, this can trigger accidents due to violating traffic regulations. This has prompted the government to take firm measures against motorists who violate regulations. Therefore we need a system that can help monitor traffic conditions. So, the aims this research is create a system that is able to detect car vehicles based on the brand and type with a high level of detection accuracy, so that it can make it easier to recognize car objects around. The detection system will be developed using the YOLO (You Only Look Once) Algorithm, because YOLO is one of the fastest and most accurate methods of object detection and is even capable of exceeding 2 times the capabilities of other algorithms. The YOLO (You Only Look Once) algorithm is an architecture of Deep Learning and an algorithm developed to detect an object in real-time. Detection is carried out on the image, and when accessing a laptop webcam, which contains a car object, uses a model from a dataset that has been trained using the Darknet framework. The detected car object will have a bounding box in the object area, and there will be a description of the car name and type and year of production in the bounding box area. Based on the classification performance test of the data that has been trained, it shows that the accuracy value reaches 92% so it can be concluded that the system can work well.


[1] A. R. Bakri, “Implementasi Intrusion Detection System (Ids) Menggunakan Telegram Sebagai Media Notifikasi,” UPN Veteran Jawa Timur, 2020.
[2] M. Harahap, J. Elfrida, P. Agusman, M. Rafael, R. Abram, and K. Andrianto, “Sistem Cerdas Pemantauan Arus Lalu Lintas Dengan DCN (Deep Convolutional Network),” in Seminar Nasional Aptikom (SEMNASTIK) 2019, 2019, pp. 367–376.
[3] B. A. Wicaksono, I. Y. Purbasari, and Y. V. Via, “Deteksi Objek Mobil dan Motor pada Lalu Lintas Berbasis Deep Learning,” JIFOSI J. Inform. dan Sist. Inf., vol. 2, no. 2, pp. 334–342, 2021.
[4] M. S. Hidayatulloh, “TA: Sistem Pengenalan Wajah Menggunakan Metode YOLO (You Only Look Once),” Universitas Dinamika, 2021.
[5] T. I. Hermanto and Y. Muhyidin, “Analisis Data Sebaran Bandwidth Menggunakan Algoritma Dbscan Untuk Menentukan Tingkat Kebutuhan Bandwidth Di Kabupaten Purwakarta,” Rabit J. Teknol. dan Sist. Inf. Univrab, vol. 5, no. 2, pp. 130–137, 2020.
[6] W. Wahyudi, A. Hidayat, M. M. Fakhri, and others, “Penerapan Machine Learning Pada Mikrokontroler Arduino Mega PRO MINI ATmega2560-16AU,” J. Embed. Syst. Secur. Intell. Syst., vol. 3, no. 1, pp. 30–39, 2022.
[7] N. P. S. Dewi, A. Y. A. Fianto, and W. Hidayat, “Penciptaan Buku Refensi Pura Mandhara Giri Semeru Agung Guna Mempopulerkan Wisata Religi Kabupaten Lumajang.”
[8] A. Amwin, “Deteksi dan Klasifikasi Kendaraan Berbasis Algoritma You Only Look Once (YOLO),” 2021.
[9] M. R. Fauzan and A. P. W. Wibowo, “Pendeteksian Plat Nomor Kendaraan Menggunakan Algoritma You Only Look Once V3 Dan Tesseract,” J. Ilm. Teknol. Infomasi Terap., vol. 8, no. 1, pp. 57–62, 2021.
[10] 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.
[11] D. Anggraini, S. A. Putri, and L. A. Utami, “Implementasi Algoritma Apriori Dalam Menentukan Penjualan Mobil Yang Paling Diminati Pada Honda Permata Serpong,” J. Media Inform. Budidarma, vol. 4, no. 2, pp. 302–308, 2020.
[12] 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.
[13] S. Ma’arif, T. Rohana, and K. Baihaqi, “Deteksi Jenis Beras Menggunakan Algoritma YOLOv3,” Sci. Student J. Information, Technol. Sci., vol. 3, no. 2, pp. 219–226, 2022.
[14] M. A. BELLA, “Implementasi Algoritma Deep Learning Untuk Sistem Deteksi Kantuk Pada Pengemudi Menggunakan Yolo,” 2021.
[15] E. F. Fernanda, “LKP: Deteksi Penyakit Kanker Payudara Menggunakan Deep Learning,” Universitas Dinamika, 2021.
[16] K. Amoako, “Healthy Hive: A Beehive Health Management Tool,” 2022.
[17] M. Romzi and B. Kurniawan, “Implementasi Pemrograman Python Menggunakan Visual Studio Code,” J. Inform. dan Komput., vol. 11, no. 2, 2020.
[18] H. Yun, “Prediction model of algal blooms using logistic regression and confusion matrix,” Int. J. Electr. Comput. Eng., vol. 11, no. 3, p. 2407, 2021.
[19] N. Amalina, “Uji akurasi aplikasi Augmented Reality pembelajaran huruf alfabet Bahasa Isyaratindonesia (BISINDO) pada Vuforia menggunakan Confusion Matrix,” Universitas Islam Negeri Maulana Malik Ibrahim, 2019.
[20] M. Hasnain, M. F. Pasha, I. Ghani, M. Imran, M. Y. Alzahrani, and R. Budiarto, “Evaluating trust prediction and confusion matrix measures for web services ranking,” IEEE Access, vol. 8, pp. 90847–90861, 2020.