ATTENTION-ENHANCED YOLOV8 FOR REAL-TIME TRAFFIC SIGN DETECTION AND RECOGNITION IN INTELLIGENT TRANSPORTATION SYSTEMS
DOI:
https://doi.org/10.54314/jssr.v9i3.6706Keywords:
Traffic Sign Detection, YOLOv8, Attention Mechanism, Intelligent Transportation Systems, Deep LearningAbstract
Abstract: Traffic sign detection and recognition (TSDR) constitute fundamental components of intelligent transportation systems (ITS) and autonomous driving technologies. Accurate and real-time recognition of traffic signs is essential for improving road safety, driver assistance systems, and autonomous vehicle navigation. However, challenges such as illumination variations, occlusions, weather conditions, small object sizes, and complex backgrounds significantly affect detection performance. This study proposes an Attention-Enhanced YOLOv8 framework for real-time traffic sign detection and recognition by integrating the Convolutional Block Attention Module (CBAM) into the YOLOv8 architecture. The proposed model aims to improve feature extraction capabilities and detection accuracy, particularly for small and partially occluded traffic signs. Experiments were conducted using the German Traffic Sign Detection Benchmark (GTSDB) dataset and additional Indonesian traffic sign datasets. The proposed model achieved a mean Average Precision (mAP@0.5) of 97.4%, precision of 96.8%, recall of 95.9%, and inference speed of 74 FPS, outperforming conventional YOLOv8 and other state-of-the-art methods. Experimental results demonstrate that the integration of attention mechanisms significantly enhances detection performance while maintaining real-time capabilities suitable for intelligent transportation applications.
Keywords: Traffic Sign Detection, YOLOv8, Attention Mechanism, Intelligent Transportation Systems, Deep Learning.
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Abstrak: Deteksi dan pengenalan rambu lalu lintas (TSDR) merupakan komponen mendasar dari sistem transportasi cerdas (ITS) dan teknologi kemudi otonom. Pengenalan rambu lalu lintas yang akurat dan berlangsung secara real-time sangat penting untuk meningkatkan keselamatan jalan, sistem bantuan pengemudi, dan navigasi kendaraan otonom. Namun, berbagai tantangan seperti variasi pencahayaan, oklusi, kondisi cuaca, ukuran objek yang kecil, dan latar belakang yang kompleks sangat memengaruhi kinerja deteksi. Penelitian ini mengusulkan kerangka kerja YOLOv8 yang diperkuat dengan mekanisme attention (Attention-Enhanced YOLOv8) untuk deteksi dan pengenalan rambu lalu lintas secara real-time dengan mengintegrasikan Convolutional Block Attention Module (CBAM) ke dalam arsitektur YOLOv8. Model yang diusulkan bertujuan untuk meningkatkan kemampuan ekstraksi fitur dan akurasi deteksi, khususnya untuk rambu lalu lintas yang berukuran kecil dan mengalami oklusi sebagian. Eksperimen dilakukan menggunakan dataset German Traffic Sign Detection Benchmark (GTSDB) dan dataset tambahan berupa rambu lalu lintas Indonesia. Model yang diusulkan mencapai nilai mean Average Precision (mAP@0.5) sebesar 97,4%, presisi 96,8%, recall 95,9%, dan kecepatan inferensi 74 FPS, yang mengungguli YOLOv8 konvensional serta metode-metode mutakhir (state-of-the-art) lainnya. Hasil eksperimen menunjukkan bahwa integrasi mekanisme attention secara signifikan meningkatkan kinerja deteksi sekaligus mempertahankan kemampuan real-time yang sesuai untuk aplikasi transportasi cerdas.
Kata kunci: Deteksi Rambu Lalu Lintas, YOLOv8, Mekanisme Attention, Sistem Transportasi Cerdas, Deep Learning.
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Copyright (c) 2026 Aliyah Aliyah, Muhammad Adhit Dwi Yuda, Iwan Iwan, Nana Marliza, Deny Rochman Arifatno, Muhamad Handika Mawardi
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