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All Journal TEKNIK Jurnal Pendidikan Teknologi dan Kejuruan Indonesian Journal of Electrical Engineering and Informatics (IJEEI) ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika JOIV : International Journal on Informatics Visualization JST ( Jurnal Sains Terapan ) JURNAL INTEGRASI JOURNAL OF APPLIED INFORMATICS AND COMPUTING J-Dinamika: Jurnal Pengabdian Kepada Masyarakat JEECAE (Journal of Electrical, Electronics, Control, and Automotive Engineering) Jurnal Teknik Mesin dan Mekatronika (Journal of Mechanical Engineering and Mechatronics) MATRIX : JURNAL MANAJEMEN TEKNOLOGI DAN INFORMATIKA Jurnal Rekayasa elektrika
Hary Oktavianto
Departemen Teknik Elektro, Politeknik Elektronika Negeri Surabaya
Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Materials Science & Nanotechnology Mathematics Mechanical Engineering Social Sciences Transportation Other

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Journal : JOURNAL OF APPLIED INFORMATICS AND COMPUTING

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YOLOv11-Based Detection of Indonesian Traffic Signs: Transfer Learning vs. From-Scratch Training Ramadhan, Ibnu Cipta; Hendriawan, Akhmad; Oktavianto, Hary
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.9718

Abstract

Traffic sign detection is a fundamental component in intelligent transportation systems (ITS), autonomous driving, and advanced driver assistance systems (ADAS), enabling vehicles to interpret road conditions and enhance safety. Developing robust traffic sign detection models for specific regions requires high-quality, well-annotated local datasets, which are often challenging and costly to create. Even when such datasets are available, training deep learning models from scratch demands substantial computational resources and time. This study compares models trained from scratch and those using transfer learning based on the lightweight YOLOv11s architecture on an Indonesian traffic sign dataset. Evaluations using precision, recall, mean Average Precision at IoU 0.5 (mAP@0.5), and mean Average Precision across IoU thresholds 0.5 to 0.95 (mAP@0.5:0.95) demonstrate that the transfer learning model consistently outperforms the from-scratch model across all metrics. These findings highlight the effectiveness and efficiency of transfer learning for developing accurate and practical traffic sign detection systems adapted to local contexts.
Co-Authors Abdillah Aziz Muntashir Abdullah Aziz Muntashir Achmad Purnomo Wijaya Amang Sudarsono, Amang angga wahyu aditya Apriyanto, R. Akbar Nur APRIYANTO, RADEN AKBAR NUR Aulia, Fira Bagus Nugraha Deby Ariyadi Bambang Sumantri BASUKI, GAMAR Bima Sena Bayu Dewantara Dadet Pramadihanto Era Purwanto Fakhruddin, Hanif Hasyier Fatekha, Rifqi Amalya GAMAR BASUKI Gamar Basuki Gigih Prabowo Handri Toar HANIF HASYIER FAKHRUDDIN Hanif Hasyier Fakhruddin Hendriawan, Akhmad Idris Winarno Izzulhaq, Muhammad Arya Kadek Reda Setiawan Suda Kisron Kisron M Udin Harun Al Rasyid, M Udin Harun Mentari Putri Jati Niam Tamami NUGROHO, MOCHAMAD ARI BAGUS R. Akbar Nur Apriyanto R. Akbar Nur Apriyanto R. Akbar Nur Apriyanto R. Oktav Yama Hendra Ramadhan, Ibnu Cipta Ridwan Ridwan Rusli, Muhammad Rizani Rusminto Tjatur Widodo Setiawan Suda, Kadek Reda Sri Muntiah Andriami Wahjoe Tjatur Sesulihatien Wirama, I Made Adiswara