DETECTION OF VEHICLE TYPE AND LICENSE PLATE WITH CONVOLUTIONAL NEURAL NETWORK MODEL YOLOV7
Abstract
This research was conducted in response to issues related to the efficiency and effectiveness of vehicle type and license plate detection. The increasingly congested traffic conditions and the expanding use of motor vehicles have posed challenges in traffic monitoring and regulation. Therefore, there is a need to develop a solution that can save time and resources while providing more comprehensive information in vehicle monitoring. This research implements the Convolutional Neural Network (CNN) algorithm with the latest YOLOv7 model from YOLO to detect vehicle types and vehicle number plates simultaneously to make it more efficient and effective, save time and resources, and provide more complete information. The research method used is Research and Development (R&D) with an experimental approach. The stages include image acquisition, labeling, dataset sharing, YOLOv7 model training, testing, prediction results, and conversion to text using Optical Character Recognition (OCR). The research results show that the ResNet34 model architecture achieves a total accuracy of 89.7% for 3x3 convolution layers and 88.6% for 5x5 convolution layers. The YOLOv5 architecture performs well on 3x3 convolution layers with an overall accuracy of 71.9%, and 58.3% for 5x5 convolution layers. However, the YOLOv7 and Mobilenet architectures tend to have lower accuracy, namely the Mobilenet architecture with a 3x3 convolution layer with a total accuracy of 63.4%, and 73.4% for the 5x5 convolution layer. Computing speed is also considered, with YOLOv5 and YOLOv7 having higher speeds than ResNet34 and Mobilenet. Tests were carried out in various lighting conditions, resulting in accurate detection of vehicle types and vehicle number plates of 90% in the morning, 85% in the afternoon and 77% at night. Overall, the system succeeded in recognizing objects with an accuracy of 84% from a total of 720 data tested, but the accuracy of converting vehicle number plates using OCR reached 22%. The results of this research demonstrate the performance and effectiveness of the YOLOv7 algorithm in detecting vehicle types and vehicle number plates, as well as providing insight into accuracy in various lighting conditions and OCR conversion.
Downloads
References
S. T. Informatika, “Implementasi Deteksi Real Time Klasifikasi Jenis Kendaraan Di Indonesia Menggunakan Metode YOLOV5,” Jurnal Pendidikan Tambusai, vol. 6, pp. 13971–13982, 2022.
R. Yudhistira, A. Pratama, H. Koesyanto, and I. Artikel, “Kejadian Kecelakaan Pada Pengemudi Ojek Online,” HIGEIA (Journal of Public Health …, vol. 4, no. Special 1, pp. 13–24, 2020.
R. Nurfauziah and H. Krisnani, “Perilaku Pelanggaran Lalu Lintas Oleh Remaja Ditinjau Dari Perspektif Konstruksi Sosial,” Jurnal Kolaborasi Resolusi Konflik, vol. 3, no. 1, p. 75, 2021,
M. Riska, “Efektivitas Penerapan E-Government Melalui Sistem E-Tilang Pada Satuan Polisi Lalu Lintas (Satlantas) Polresta Pekanbaru,” J Chem Inf Model, vol. 6, no. 9, pp. 1689–1699, 2019.
A. Fadli, A. R. Razak, and M. Tahir, “Kinerja Polisi Lalu Lintas Dalam Sosialisasi E-Tilang Di Kota Makassar,” Kajian Ilmiah Mahasiswa Administrasi Publik (KIMAP), vol. 2, no. 2, pp. 742–755, 2021.
A. Haryono, S. Bismantoko, G. M. Putra, and T. Widodo, “Accuracy in Object Detection based on Image Processing at the Implementation of Motorbike Parking on the Street,” Proceedings of the 2019 2nd International Conference on Applied Engineering, ICAE 2019, pp. 0–4, 2019,
A. Baccar, M. Ali, and B. Ayed, “Automatic Number Plate Recognition System based on Deep Learning Ousseme Kriaa 1 PUBLICATION 4 CITATIONS SEE PROFILE,” no. April, 2020.
A. F. Fandisyah, N. Iriawan, and W. S. Winahju, “Deteksi Kapal di Laut Indonesia Menggunakan YOLOv3,” Jurnal Sains dan Seni ITS, vol. 10, no. 1, 2021,
R. Akbar, Weriana, R. A. Siroj, and M. W. Afgani, “Experimental Reseacrch Dalam Metodologi Pendidikan,” Jurnal Ilmiah Wahana Pendidikan, Januari, vol. 2023, no. 2, pp. 465–474, 2023.
I. H. Al amin and A. Aprilino, “Implementasi Algoritma Yolo Dan Tesseract Ocr Pada Sistem Deteksi Plat Nomor Otomatis,” Jurnal Teknoinfo, vol. 16, no. 1, p. 54, 2022,
M. A. Jawale, P. William, A. B. Pawar, and N. Marriwala, “Implementation of number plate detection system for vehicle registration using IOT and recognition using CNN,” Measurement: Sensors, vol. 27, Jun. 2023,
S. G. Z. & S. Suhartono, “Sistem Objek Recognition Plat Nomor Kendaraan Untuk Sistem Parkir Bandara,” Jessi, vol. 03, no. November, pp. 127–134, 2022.
J. Tang, L. Wan, J. Schooling, P. Zhao, J. Chen, and S. Wei, “Automatic number plate recognition (ANPR) in smart cities: A systematic review on technological advancements and application cases,” Cities, vol. 129, Oct. 2022,
F. Sultan, K. Khan, Y. A. Shah, M. Shahzad, U. Khan, and Z. Mahmood, “Towards Automatic License Plate Recognition in Challenging Conditions,” Applied Sciences (Switzerland), vol. 13, no. 6, Mar. 2023,
R. Rathi, A. Sharma, N. Baghel, P. Channe, S. Barve, and S. Jain, “License plate detection using YOLO v4,” Int J Health Sci (Qassim), pp. 9456–9462, May 2022,
D. Setiawan, D. W. Udjiyanto, and A. Syari’udin, “Analisis faktor-faktor yang mempengaruhi tingkat kemiskinan di dua zona wilayah indonesia (WIB & WITA) tahun 2014-2020 menggunakan model ekonometrika data panel dinamis dengan pendekatan Generalized Method of Moment Arellano-Bond,” Jurnal Ekonomi dan Bisnis, vol. 11, no. 1, pp. 361–367, 2022.
N. Purwanto, “Variabel Dalam Penelitian Pendidikan,” Jurnal Teknodik, vol. 6115, pp. 196–215, 2019,
Ningsih, “Hubungan Media Pembelajaran dengan Peningkatan Siswa Pada Mata Pelajaran PAI di SMP Iptek Sengkol Tangerang Selatan,” Tarbawai: Jurnal Pendidikan Agama Islam, vol. 6, no. 01, pp. 77–92, 2021.
S. Elfwing, E. Uchibe, and K. Doya, “Sigmoid-weighted linear units for neural network function approximation in reinforcement learning,” Neural Networks, vol. 107, pp. 3–11, Nov. 2018,
R. T. Handayanto and H. Herlawati, “Prediksi Kelas Jamak dengan Deep Learning Berbasis Graphics Processing Units,” Jurnal Kajian Ilmiah, vol. 20, no. 1, pp. 67–76, Jan. 2020,
N. D. Miranda, L. Novamizanti, S. Rizal, F. T. Elektro, and U. Telkom, “Convolutional Neural Network Pada Klasifikasi Sidik Jari Menggunakan Resnet-50 Classification of Fingerprint Pattern Using Convolutional Neural Network in Clahe Image,” Jurnal Teknik Informatika (JUTIF), vol. 1, no. 2, pp. 61–68, 2020.
K. Khairunnas, E. M. Yuniarno, and A. Zaini, “Pembuatan Modul Deteksi Objek Manusia Menggunakan Metode YOLO untuk Mobile Robot,” Jurnal Teknik ITS, vol. 10, no. 1, 2021,
Y. A. Usen and C. Hayat, “Design and Build Vehicle Plate Detection System Using You Only Look Once Method Based on Android,” Jurnal Teknik Informatika (Jutif), vol. 4, no. 4, pp. 807–818, 2023,
C.-Y. Wang, A. Bochkovskiy, and H.-Y. M. Liao, “YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors,” Cornell University, pp. 1–15, 2022.
J. P. N. L. Chairisni, “Sistem Pendeteksian Dan Pengenalan Ekspresi Wajah Dengan Algoritma Yolo Dan Convolutional Neural Network,” Universitas Tarumanagara, p. 255, 2020.
A. Amwin, “Deteksi Dan Klasifikasi Kendaraan Berbasis Algoritma You Only Look Once (Yolo),” 2021. Accessed: Oct. 11, 2023. [Online].
Copyright (c) 2024 Suhartono Suhartono, Satria Gunawan Zain, Andi Ardilla
This work is licensed under a Creative Commons Attribution 4.0 International License.
