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All Journal Jurnal Algoritma Proceeding of International Conference Health, Science And Technology (ICOHETECH)
Harsanto, Harsanto
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Computer Science & IT Decision Sciences, Operations Research & Management Health Professions Industrial & Manufacturing Engineering Medicine & Pharmacology

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Deteksi Rambu Lalu Lintas Real-Time di Indonesia dengan Penerapan YOLOv11: Solusi Untuk Keamanan Berkendara Pradana, Afu Ichsan; Harsanto, Harsanto; Wijiyanto, Wijiyanto
Jurnal Algoritma Vol 21 No 2 (2024): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.21-2.2106

Abstract

This research aims to formulate and assess a real-time traffic sign detection framework in the context of Indonesia, using YOLOv11. Given the heterogeneous nature of traffic signs and road conditions in Indonesia, there is an urgent need for a robust and precise model to improve driving safety. The findings show that the model successfully achieved a Mean Average Precision (mAP) of 0.99, simultaneously demonstrating high accuracy across a wide range of traffic sign classifications. Evaluation using Confusion Matrix, shed light on the negligible error rate, signaling that the model has sufficient reliability for real-world applications. The potential applications of this technology are crucial in strengthening Indonesia's driving safety and intelligent transportation systems.
Intelligent Traffic Sign Detection Using Yolov9 Pradana, Afu Ichsan; Harsanto, Harsanto; Maulindar, Joni
Proceeding of the International Conference Health, Science And Technology (ICOHETECH) 2024: Proceeding of the 5th International Conference Health, Science And Technology (ICOHETECH)
Publisher : LPPM Universitas Duta Bangsa Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47701/icohetech.v5i1.4205

Abstract

This research examines the automatic detection and classification of traffic signs using artificial intelligence (AI) and computer vision technologies. As urban traffic increases, quickly and accurately recognizing traffic signs becomes a challenge, especially under adverse conditions such as bad weather and limited visibility. Conventional technologies that rely on human vision are prone to errors, so an automated solution is needed. This research uses the YOLOv9 algorithm for real-time traffic sign detection, utilizing the Generalized ELAN (GELAN) architecture that combines the advantages of CSPNet and ELAN for efficiency and accuracy. The dataset used consists of 1924 images processed through various stages, including data augmentation and normalization. The model was trained for 15 epochs with fairly high accuracy results in the prohibitory, danger, and mandatory sign categories. However, there were still some misclassifications, especially in the prohibitory category which was sometimes mistakenly detected as another category or background. Overall, the model performed well in detecting traffic signs in various environmental conditions, but still needs improvement to increase accuracy in certain cases.
Co-Authors Maulindar, Joni Pradana, Afu Ichsan Wijiyanto Wijiyanto, Wijiyanto