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Penelitian ini bertujuan untuk merumuskan dan menilai kerangka deteksi rambu lalu lintas real-time dalam konteks di negara Indonesia, menggunakan YOLOv11. Mengingat sifat heterogen rambu-rambu lalu lintas dan kondisi jalan raya di Indonesia, ada kebutuhan mendesak untuk model yang tangguh dan tepat guna meningkatkan keselamatan berkendara. Metode yang digunakan adalah YOLOv11, sebuah algoritma deep learning yang dirancang untuk deteksi objek dengan kecepatan tinggi dan akurasi tinggi. Algoritma ini memproses gambar dalam satu langkah inferensi dan memiliki arsitektur yang dioptimalkan untuk pengenalan real-time. Proses meliputi preprocessing data seperti augmentasi dan anotasi, pelatihan model menggunakan dataset rambu lalu lintas Indonesia, serta evaluasi performa model menggunakan metrik precision, recall, dan mean average precision (mAP). Temuan menunjukkan bahwa model berhasil mencapai mAP 0,99, dengan akurasi tinggi di berbagai klasifikasi rambu lalu lintas. Evaluasi menggunakan Confusion Matrix menunjukkan tingkat kesalahan yang dapat diabaikan, menandakan keandalan model untuk aplikasi dunia nyata. Aplikasi potensial teknologi ini sangat penting dalam memperkuat sistem keselamatan berkendara dan transportasi cerdas di Indonesia.
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References
- Kementerian Perhubungan Indonesia, “Laporan Statistik Kecelakaan Lalu Lintas Tahun 2021.” 2022.
- Polri, “Korban Meninggal Kecelakaan Lalu Lintas Mayoritas Usia Produktif,” Korlantas Polri, Oktober 2024.
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- R. Mahadshetti, J. Kim, and T.-W. Um, “Sign-YOLO: Traffic Sign Detection Using Attention-Based YOLOv7,” IEEE Access, vol. 12, pp. 132689–132700, 2024, doi: 10.1109/ACCESS.2024.3417023.
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- Y. Tian, J. Gelernter, X. Wang, J. Li, and Y. Yu, “Traffic Sign Detection Using a Multi-Scale Recurrent Attention Network,” IEEE Trans. Intell. Transp. Syst., vol. 20, no. 12, pp. 4466–4475, 2019, doi: 10.1109/TITS.2018.2886283.
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- M. Beyersdorff and et al., “YOLOv8: Towards the Next Generation of Real-Time Object Detection,” 2023.
- J. Redmon and A. Farhadi, “YOLOv3: An Incremental Improvement,” ArXiv Prepr. ArXiv180402767, vol. 1, pp. 1–6, 2016.
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- R. Khanam and M. Hussain, “YOLOv11: An Overview of the Key Architectural Enhancements,” Oct. 23, 2024, arXiv: arXiv:2410.17725. Accessed: Oct. 30, 2024. [Online]. Available: http://arxiv.org/abs/2410.17725
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- A. A. Khalifa, W. M. Alayed, H. M. Elbadawy, and R. A. Sadek, “Real-Time Navigation Roads: Lightweight and Efficient Convolutional Neural Network (LE-CNN) for Arabic Traffic Sign Recognition in Intelligent Transportation Systems (ITS),” Appl. Sci., vol. 14, no. 9, p. 3903, May 2024, doi: 10.3390/app14093903.
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- Muh. Ikbal, “Traffic sign in indonesia.” 2024. [Online]. Available: https://www.kaggle.com/datasets/ikbal12082004/traffic-sign-in-indonesia/data
- G. Oreski, “YOLO*C — Adding context improves YOLO performance,” Neurocomputing, vol. 555, p. 126655, Oct. 2023, doi: 10.1016/j.neucom.2023.126655.
- Y. Cui, D. Guo, H. Yuan, H. Gu, and H. Tang, “Enhanced YOLO Network for Improving the Efficiency of Traffic Sign Detection,” Appl. Sci., vol. 14, no. 2, p. 555, Jan. 2024, doi: 10.3390/app14020555.
- J. Redmon and A. Farhadi, “Advancements in YOLO Object Detection for Real-World Applications,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 45, no. 6, pp. 1254–1265, 2023.
- M. Satria and H. Permadi, “Kecerdasan Buatan dan Masa Depan Transportasi di Indonesia,” J. Teknol. Transp., vol. 9, no. 4, pp. 298–310, 2021.
- L. Purwanto and W. Riyanto, “Tantangan dan Peluang dalam Implementasi Sistem Deteksi Rambu di Indonesia,” J. Inov. Transp., vol. 5, no. 1, pp. 23–35, 2022.
- D. Chicco and G. Jurman, “The Advantages of the Matthews Correlation Coefficient (MCC) over F1 Score and Accuracy in Binary Classification Evaluation,” BMC Genomics, vol. 21, no. 1, p. 6, 2020.
References
Kementerian Perhubungan Indonesia, “Laporan Statistik Kecelakaan Lalu Lintas Tahun 2021.” 2022.
Polri, “Korban Meninggal Kecelakaan Lalu Lintas Mayoritas Usia Produktif,” Korlantas Polri, Oktober 2024.
Muh. Ikbal and R. A. Saputra, “PENGENALAN RAMBU LALU LINTAS MENGGUNAKAN METODE YOLOV8,” JIKA J. Inform., vol. 8, no. 2, p. 204, Apr. 2024, doi: 10.31000/jika.v8i2.10609.
E. Hrustic, Z. Xu, and D. Vivet, “Deep Learning Based Traffic Signs Boundary Estimation,” IEEE Intell. Veh. Symp. Proc., no. Iv, pp. 451–456, 2020, doi: 10.1109/IV47402.2020.9304590.
J. Cao, J. Zhang, and X. Jin, “A Traffic-Sign Detection Algorithm Based on Improved Sparse R-cnn,” IEEE Access, vol. 9, pp. 122774–122788, 2021, doi: 10.1109/ACCESS.2021.3109606.
R. Mahadshetti, J. Kim, and T.-W. Um, “Sign-YOLO: Traffic Sign Detection Using Attention-Based YOLOv7,” IEEE Access, vol. 12, pp. 132689–132700, 2024, doi: 10.1109/ACCESS.2024.3417023.
R. K. Megalingam, K. Thanigundala, S. R. Musani, H. Nidamanuru, and L. Gadde, “Indian traffic sign detection and recognition using deep learning,” Int. J. Transp. Sci. Technol., vol. 12, no. 3, pp. 683–699, Sep. 2023, doi: 10.1016/j.ijtst.2022.06.002.
N. Triki, M. Karray, and M. Ksantini, “A Real-Time Traffic Sign Recognition Method Using a New Attention-Based Deep Convolutional Neural Network for Smart Vehicles,” Appl. Sci., vol. 13, no. 8, p. 4793, Apr. 2023, doi: 10.3390/app13084793.
I. J. Thira, D. Riana, A. N. Ilhami, B. R. S. Dwinanda, and H. Choerunisya, “Pengenalan Alfabet Sistem Isyarat Bahasa Indonesia (SIBI) Menggunakan Convolutional Neural Network,” J. Algoritma, vol. 20, no. 2, pp. 421–432, Oct. 2023, doi: 10.33364/algoritma/v.20-2.1480.
X. Cao, Y. Xu, J. He, J. Liu, and Y. Wang, “A Lightweight Traffic Sign Detection Method With Improved YOLOv7-Tiny,” IEEE Access, vol. 12, pp. 105131–105147, 2024, doi: 10.1109/ACCESS.2024.3435384.
Y. Tian, J. Gelernter, X. Wang, J. Li, and Y. Yu, “Traffic Sign Detection Using a Multi-Scale Recurrent Attention Network,” IEEE Trans. Intell. Transp. Syst., vol. 20, no. 12, pp. 4466–4475, 2019, doi: 10.1109/TITS.2018.2886283.
W. Farag, “Recognition of traffic signs by convolutional neural nets for self-driving vehicles,” Int. J. Knowl.-Based Intell. Eng. Syst., vol. 22, no. 3, pp. 205–214, 2018, doi: 10.3233/KES-180385.
A. Wang et al., “YOLOv10: Real-Time End-to-End Object Detection,” May 23, 2024, arXiv: arXiv:2405.14458. Accessed: Sep. 16, 2024. [Online]. Available: http://arxiv.org/abs/2405.14458
M. Beyersdorff and et al., “YOLOv8: Towards the Next Generation of Real-Time Object Detection,” 2023.
J. Redmon and A. Farhadi, “YOLOv3: An Incremental Improvement,” ArXiv Prepr. ArXiv180402767, vol. 1, pp. 1–6, 2016.
M. Hussain, “YOLOv1 to v8: Unveiling Each Variant–A Comprehensive Review of YOLO,” IEEE Access, vol. 12, pp. 42816–42833, 2024, doi: 10.1109/ACCESS.2024.3378568.
R. Khanam and M. Hussain, “YOLOv11: An Overview of the Key Architectural Enhancements,” Oct. 23, 2024, arXiv: arXiv:2410.17725. Accessed: Oct. 30, 2024. [Online]. Available: http://arxiv.org/abs/2410.17725
A. Mulyanto, R. I. Borman, P. Prasetyawan, W. Jatmiko, P. Mursanto, and A. Sinaga, “Indonesian Traffic Sign Recognition for Advanced Driver Assistent (ADAS) Using YOLOv4,” 2020 3rd Int. Semin. Res. Inf. Technol. Intell. Syst. ISRITI 2020, pp. 520–524, 2020, doi: 10.1109/ISRITI51436.2020.9315368.
A. A. Khalifa, W. M. Alayed, H. M. Elbadawy, and R. A. Sadek, “Real-Time Navigation Roads: Lightweight and Efficient Convolutional Neural Network (LE-CNN) for Arabic Traffic Sign Recognition in Intelligent Transportation Systems (ITS),” Appl. Sci., vol. 14, no. 9, p. 3903, May 2024, doi: 10.3390/app14093903.
C. Han, G. Gao, and Y. Zhang, “Real-time small traffic sign detection with revised faster-RCNN,” Multimed. Tools Appl., vol. 78, no. 10, pp. 13263–13278, 2019, doi: 10.1007/s11042-018-6428-0.
Muh. Ikbal, “Traffic sign in indonesia.” 2024. [Online]. Available: https://www.kaggle.com/datasets/ikbal12082004/traffic-sign-in-indonesia/data
G. Oreski, “YOLO*C — Adding context improves YOLO performance,” Neurocomputing, vol. 555, p. 126655, Oct. 2023, doi: 10.1016/j.neucom.2023.126655.
Y. Cui, D. Guo, H. Yuan, H. Gu, and H. Tang, “Enhanced YOLO Network for Improving the Efficiency of Traffic Sign Detection,” Appl. Sci., vol. 14, no. 2, p. 555, Jan. 2024, doi: 10.3390/app14020555.
J. Redmon and A. Farhadi, “Advancements in YOLO Object Detection for Real-World Applications,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 45, no. 6, pp. 1254–1265, 2023.
M. Satria and H. Permadi, “Kecerdasan Buatan dan Masa Depan Transportasi di Indonesia,” J. Teknol. Transp., vol. 9, no. 4, pp. 298–310, 2021.
L. Purwanto and W. Riyanto, “Tantangan dan Peluang dalam Implementasi Sistem Deteksi Rambu di Indonesia,” J. Inov. Transp., vol. 5, no. 1, pp. 23–35, 2022.
D. Chicco and G. Jurman, “The Advantages of the Matthews Correlation Coefficient (MCC) over F1 Score and Accuracy in Binary Classification Evaluation,” BMC Genomics, vol. 21, no. 1, p. 6, 2020.