Article Sidebar

Submitted
November 30, 2024
Accepted
December 6, 2024
Published
December 8, 2024
Download
PDF
Statistic
Read Counter : 0 Download : 0

Main Article Content

Abstract

Vehicle detection plays a crucial role in various applications such as traffic surveillance, license plate recognition, and the development of autonomous vehicles. The You Only Look Once (YOLO) object detection method is renowned for its high-speed real-time object detection capabilities. In this study, YOLO is employed to detect vehicles in images and videos. YOLO treats object detection as a direct regression problem for bounding boxes and class predictions. The aim of this research is to develop a vehicle counting system using the YOLO method. The Midpoint algorithm is utilized to calculate the midpoint between two points in a coordinate plane. Another objective is to analyze the strengths and weaknesses of the method and algorithm in the context of vehicle detection while identifying related research trends. The test results indicate that the system is capable of detecting vehicles with an average accuracy of 92.42% across four different time periods. In the morning, the system detected 156 vehicles (manual count: 147, accuracy: 94.23%); at midday, it detected 246 vehicles (manual count: 225, accuracy: 91.46%); in the evening, 377 vehicles were detected (manual count: 351, accuracy: 93.10%); and at night, the system identified 526 vehicles (manual count: 225, accuracy: 92.58%). This study contributes to the development of a more effective vehicle counting system for smart city applications while also paving the way for further research on vehicle detection under varying lighting and environmental conditions.

Keywords

Accuracy, CNN, Hungarian, Midpoint, Yolo

Article Details

License

Copyright (c) 2024 TEPIAN

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite
Septian, M. R. D., Masitoh, A. H., & Sari, I. M. (2024). Implementation of Deep Learning Algorithm for Vehicle Count Monitoring System. TEPIAN, 5(4), 143–149. https://doi.org/10.51967/tepian.v5i4.3213
Download Citation
Endnote/Zotero/Mendeley (RIS)
BibTeX

References

  1. Adianto, F. K. (2021). Deteksi Kantuk Menggunakan Pendekatan Deep Learning Secara Real-time. Institut Teknologi Sepuluh Nopember.
  2. Arif, M. F., Nurkholis, A., Laia, S., & Rosyani, P. (2023). Deteksi Kendaraan Dengan Metode YOLO. AI Dan SPK: Jurnal Artificial Intelligent Dan Sistem Penunjang Keputusan, 1(1), 20–27.
  3. Aryanto, R., Rosid, M. A., & Busono, S. (2023). Penerapan Deep Learning untuk Pengenalan Tulisan Tangan Bahasa Aksara Lota Ende dengan Menggunakan Metode Convolutional Neural Networks. Jurnal Informasi Dan Teknologi, 258–264.
  4. Barat, B. P. S. J. (2023). Jumlah Kendaraan Bermotor Menurut Kabupaten/Kota dan Jenis Kendaraan di Provinsi Jawa Barat (unit), 2023. In https://jabar.bps.go.id/id/statistics-table/3/VjJ3NGRGa3dkRk5MTlU1bVNFOTVVbmQyVURSTVFUMDkjMw==/jumlah-kendaraan-bermotor-menurut-kabupaten-kota-dan-jenis-kendaraan-di-provinsi-jawa-barat–unit—2023.html?year=2023.
  5. Christian, J., & Al Idrus, S. I. (2023). Introduction to Citrus Fruit Ripens Using the Deep Learning Convolutional Neural Network (CNN) Learning Method. Asian Journal of Applied Education (AJAE), 2(3), 459–470.
  6. Hegarini, E., Saifullah, A. A., & Wardijono, B. A. (2024). Sistem Penghitung Jumlah Kendaraan dan Pendeteksi Kecepatan pada Ruas Jalan Menggunakan Metode Haar Cascade Classifier. Jurnal SIKOMTEK, 14(01), 95–100.
  7. Kim, J.-H., Kim, N., & Won, C. S. (2023). High-speed drone detection based on yolo-v8. ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 1–2.
  8. Niu, H., Liu, J., Yu, Z., Zheng, D., He, P., & Wang, F. (2022). Real-time object tracking system using PTZ camera. 2022 IEEE 4th International Conference on Civil Aviation Safety and Information Technology (ICCASIT), 471–478.
  9. Nugroho, G. S., & Hazmin, G. (2022). Perbandingan Algoritma untuk Mereduksi Noise pada Citra Digital. Journal of Information Technology Ampera, 3(2), 159–174.
  10. Nugroho, L. A., Latifa, E. A., & Maulani, E. O. (2024). Dampak jumlah kendaraan besar terhadap kemacetan lalu lintas di jalan tol. JURNAL TEKNIK SIPIL CENDEKIA (JTSC), 5(2), 915–928.
  11. Putri, V. H. (2023). Detecting Incoming and Outgoing Passengers on Intelligent Car (iCar Its) Using Computer Vision. Institut Teknologi Sepuluh Nopember.
  12. Rachmawati, F., & Widhyaestoeti, D. (2020). Deteksi Jumlah Kendaraan di Jalur SSA Kota Bogor Menggunakan Algoritma Deep Learning YOLO. Prosiding LPPM Uika Bogor.
  13. Ramadhani, F., Satria, A., & Dewi, S. (2024). Identifikasi Kendaraan Bermotor pada Dashcam Mobil Menggunakan Algoritma YOLO. Hello World Jurnal Ilmu Komputer, 2(4), 199–206.
  14. Rasywir, E., Sinaga, R., & Pratama, Y. (2020). Evaluasi pembangunan sistem pakar penyakit tanaman sawit dengan metode deep neural network (DNN). Jurnal Media Informatika Budidarma, 4(4), 1206–1215.
  15. Sama, A. K., & Sharma, A. (2023). Simulated uav dataset for object detection. ITM Web of Conferences, 54, 2006.
  16. Syarif, H., & others. (2023). Evaluasi Tingkat Visual Attention Mahasiswa pada Pembelajaran Online dengan Meeting Zoom Menggunakan Metode Histogram of Oriented Gradients (HOG)= Evaluation Of Students Visual Attention Levels In Online Learning With Meeting Zoom Using The Histogram Of Oriented Gradients (Hog) Method. Universitas Hasanuddin.
  17. Tamsir, N., Soetikno, Y. J. W., & others. (2021). Aplikasi Penjualan Baju Kaos Berbasis Web dan Android. SISITI: Seminar Ilmiah Sistem Informasi Dan Teknologi Informasi, 10(1), 1–8.
  18. Ünlü, Ü. C. (2021). Improvement on motion-guided siamese object tracking networks using prioritized windows. Izmir Institute of Technology (Turkey).
  19. Wahyuni, S., & Sulaeman, M. (2022). Penerapan algoritma deep learning untuk sistem absensi kehadiran deteksi wajah di PT Karya Komponen Presisi. Jurnal Informatika SIMANTIK, 7(1), 12–21.
  20. Widyawan, S., & others. (2019). Analisis Kinerja Simpang Bersinyal Untuk Meningkatkan Keselamatan Pada Simpang Depok Kota Depok. AIRMAN: Jurnal Teknik Dan Keselamatan Transportasi, 2(1), 30–38.
  21. Yu, J., Xu, J., Chen, Y., Li, W., Wang, Q., Yoo, B., & Han, J.-J. (2021). Learning generalized intersection over union for dense pixelwise prediction. International Conference on Machine Learning, 12198–12207