Sistem Deteksi Lampu Lalu Lintas Sebagai Asisten Pengemudi Menggunakan Convolutional Neural Network

Akhmad Hendriawan, Muhammad Iqbal Millyniawan Pradana, Ronny Susetyoko

Abstract


Accident cases in Indonesia are increasing along with the increase in the number of motorized vehicles. From 2016 to 2017, speed limit violations increased by 96.20% and violations of road markings or signs also increased by 5.54%. Intelligent transportation system is one solution to reduce the number of accidents. Currently Driver Assistance Systems (DAS) are being developed in the automotive world. The purpose of this research is to design a watershed based on three input parameters for determining recommended actions, namely: 1) distance to the vehicle behind; 2) vehicle speed; and 3) traffic light status with recommendation action using fuzzy rule base. Lidar sensor for distance detection and GPS for monitoring vehicle speed. The YOLOv4 Algorithm method is used to detect traffic lights. The results of this study, the accuracy of sign color recognition is 92.831% with a detection speed of up to 8.94 FPS. The most stable reading distance is between 1 – 8-meters with a light intensity of 10 – 3200 lux and a tilt angle of up to 90 degrees. There is a drop in processing speed of up to 1.5 FPS during system integration. This DAS is effective enough to be applied to two-wheeled and fourwheeled motorized vehicles.

References


F. Pangestu, A. W. Widodo, and B. Rahayudi, “Prediksi Jumlah Kendaraan Bermotor di Indonesia Menggunakan Metode Average-Based Fuzzy Time Series Models,†J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 2, no. 9, pp. 2923–2929, 2018.

U. Enggarsasi and N. K. Sa’diyah, “Kajian Terhadap Faktor-Faktor Penyebab Kecelakaan Lalu Lintas Dalam Upaya Perbaikan Pencegahan Kecelakaan Lalu Lintas,†Perspektif, vol. 22, no. 3, p. 228, 2017, doi: 10.30742/perspektif.v22i3.632.

M. F. T. Hakim, “Rancang Bangun Sistem Deteksi Rambu – Rambu Lalu Lintas Menggunakan Jaringan Syaraf,†2018.

G. De-Las-Heras, J. Sánchez-Soriano, and E. Puertas, “Advanced driver assistance systems (ADAS) based on machine learning techniques for the detection and transcription of variable message signs on roads,†Sensors, vol. 21, no. 17, pp. 1–18, 2021, doi: 10.3390/s21175866.

L. Wang et al., “Advanced Driver-Assistance System (ADAS) for Intelligent Transportation Based on the Recognition of Traffic Cones,†Adv. Civ. Eng., vol. 2020, 2020, doi: 10.1155/2020/8883639.

M. Kumar, D. Dhake, G. Palde, and U. Mandawkar, “DETECTION OF TRAFFIC SIGNS BY CONVOLUTIONAL NEURAL NETWORK USING SEQUENTIAL API,†vol. 9, no. 6, pp. 177–181, 2021.

O. R. Sitanggang, H. Fitriyah, and F. Utaminingrum, “Sistem Deteksi dan Pengenalan Jenis Rambu Lalu Lintas Menggunakan Metode Shape Detection Pada Raspberry Pi,†J. Pengemb. Teknol. Inf. dan Ilmu Komput. Univ. Brawijaya, vol. 2, no. 12, 2018.

M. Harahap et al., “Sistem Cerdas Pemantauan Arus Lalu Lintas Dengan YOLO (You Only Look Once v3),†Semin. Nas. APTIKOM, p. 2019, 2019.

D. Okky Deltania and E. Apriaskar, “Pengaturan Lampu Lalu Lintas (Traffic Light) Dengan Sensor Ultrasonik,†J. Ilm. Tek. Elektro, vol. 19, no. 1, pp. 77–95, 2021, [Online]. Available: http://dx.doi.org/10.25105/jetri.v19i1.8660.

W. Sugeng, T. D. Putri, and H. Al Kamal, “Development of GPS-Based Mobile Application for Motorized Vehicle Speed Survey,†J. Pekommas, vol. 4, no. 2, p. 147, 2019, doi: 10.30818/jpkm.2019.2040205.

H. Gao, W. Wang, C. Yang, W. Jiao, Z. Chen, and T. Zhang, “Traffic signal image detection technology based on YOLO,†J. Phys. Conf. Ser., vol. 1961, no. 1, 2021, doi: 10.1088/1742-6596/1961/1/012012.

A. Bochkovskiy, C.-Y. Wang, and H.-Y. M. Liao, “YOLOv4: Optimal Speed and Accuracy of Object Detection,†2020, [Online]. Available: http://arxiv.org/abs/2004.10934.

Q. Wang, Q. Zhang, X. Liang, Y. Wang, C. Zhou, and V. I. Mikulovich, “Improved YOLOv4 Algorithm,†pp. 1–20, 2022.

A. K. Panggabean, A. Syahfaridzah, and N. A. Ardiningih, “Warna Hsv Menggunakan Aplikasi Matlab,†vol. 4, no. 2, pp. 94–97, 2020.




DOI: https://doi.org/10.35314/isi.v8i1.3155

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