Prototype Traffic Light Control System Based on Ambulance Objects Using YOLO Method
DOI:
https://doi.org/10.33795/jartel.v15i2.6184Keywords:
Ambulance, Camera, Image Processing, Speaker, Traffic Light, YOLOAbstract
Ambulances are emergency service vehicles, but they are often caught in traffic jams that can potentially cause death to patients due to the need for quick action. Then, there was a case of an ambulance accident at a traffic intersection when requesting priority access to the road because it was carrying a corpse. With a traffic light control system using the YOLOv8 image processing method as an ambulance car detector and an audio warning signal output, it can provide warnings to prevent accidents at traffic intersections and prioritize ambulances to arrive faster to their destination. In the system, the ambulance is detected in front and rear view with a minimum confidence value of 70% to send a command to the ESP32 to activate or turn off the output. The ambulance detection system based on distance obtained a confidence value of 95% at a distance of 30cm, where the system is more accurate because the object is closer to the camera. Then, the detection system with light and speed obtained an average confidence value of 78.3% which is getting lower at night due to poor lighting conditions, in the object speed factor obtained a confidence value of 89% at a speed of 40km / hour. The confidence value is also influenced by the angle of the camera position when detecting objects, at an angle of 45° obtained a value of 89% and an angle of 135° with a value of 82%. The system is able to distinguish ambulances with similar objects, other objects detected obtained a confidence value of 43% - 56%. There is a delay in object recognition, the average delay value is 18.9 m/s which is influenced by computer performance. In the overall system delay there is an average delay value of 3 seconds. In order for the system to control traffic lights and output on time, it is determined that the minimum detection distance is 66.6m which is influenced by a delay of 3 seconds and an ambulance speed of 22.2 m/s.
References
Republik Indonesia, Undang-Undang Republik Indonesia Nomor 22 Tahun 2009 Pasal 134 tentang Lalu Lintas Angkutan Jalan. Jakarta: Lembaran RI, 2009.
Menteri Kesehatan dan Kesejahteraan Sosial, Keputusan Menteri Kesehatan dan Kesejahteraan Sosial Nomor 143 Tahun 2001 tentang Standarisasi Kendaraan Pelayanan Medik. Jakarta: Warta Perundang-undangan Nomor 2054, 2001.
I. A. Qurni, "Analisis Daerah Rawan Kecelakaan Lalu Lintas (Studi Kasus Jalan Nasional Arteri Primer Dan Arteri Sekunder Kabupaten Kendal)," Jurnal Geografi: Media Informasi Pengembangan Dan Profesi Kegeografian, vol. 13, no. 1, pp. 53-60, 2019.
F. Zulianti, R. Munadi, and I. H. Santoso, "Smart Traffic Light Berbasis Internet Of Things Pada Keselamatan Ambulans," e-Proceeding of Engineering, 2021.
K. Kurniawan, "Kecelakaan Ambulans Vs Datsun di Kota Malang, Petugas Pemakaman PDP Covid-19 Dalam Ambulan Pingsan," tribunnews.com, Malang, 2020.
M. Sarosa, N. Muna, and E. Rohadi, "Detection of natural disaster victims using You Only Look Once (YOLO)," dalam IOP Conference Series: Materials Science and Engineering, vol. 1098, 2021.
C. Geraldy and C. Lubis, "Pendeteksian dan Pengenalan Jenis Mobil Menggunakan Algoritma You Only Look Once dan Convolutional Neural Network," Jurnal Ilmu Komputer dan Sistem Informasi, pp. 197-199, 2020.
P. G. Pamungkas, K. and H. Al Fatta, "Deteksi Mobil Ambulance Menggunakan Operator Sobel," Jurnal Teknologi Informasi dan Komunikasi, vol. 10, no. 1, p. 87, 2020.
A. M. Ario Saputra, A. Mulyana, and S. Aulia, "Sistem Kontrol Lampu Lalu Lintas Berdasarkan Kepadatan Kendaraan Menggunakan Image Processing," Journal e-Proceeding of Applied Science, p. 3321, 2021.
M. Ardiana, T. Dutono, and B. Santoso, "Gender Classification Based Speaker’s Voice using YIN Algorithm and MFCC," International Electronics Symposium (IES), pp. 438-444, 2021.
M. H. Ibrahim, I. I. Tritoasmoro, and L. Novamizanti, "Implementasi Sistem Kontrol Lampu Lalu Lintas Berdasarkan Suara Sirene Pada Arduino," JURTEKSI (Jurnal Teknologi dan Sistem Informasi), p. 3667, 2020.
Usman, P. Albert, I. V. Sari, I. Idris, and R. Khair, "Rancang Bangun Traffic Light System Tanggap Darurat Berbasis IoT," Jurnal Sistem Komputer dan Informatika (JSON), pp. 195-199, 2020.
Q. Aini, N. Lutfiani, H. Kusumah, and S. Zahran, "Deteksi Dan Pengenalan Objek Dengan Model Machine Learning Model YOLOv5," Journal of Computer Engineering System and Science, vol. 6, no. 2, p. 192, 2021.
D. I. Mulyana and M. A. Rofik, "Implementasi Deteksi Real Time Klasifikasi Jenis Kendaraan Di Indonesia Menggunakan Metode YOLOV5," Jurnal Pendidikan Tambusai, pp. 13971-13982, 2022.
R. M. Taufiq, Sunanto, and Y. Rizki, "Integrated Smart Traffic Control System Menuju Pekanbaru Sebagai Smart City," JURTEKSI (Jurnal Teknologi dan Sistem Informasi), pp. 67–74, 2020.
A. Hilmani and L. Hassouni, "Automated Real-Time Intelligent Traffic Control System for Smart Cities Using Wireless Sensor Networks," Wireless Communications and Mobile Computing, vol. 2020, no. 1, 2020.
