YOLOv11-Based Detection of Indonesian Traffic Signs: Transfer Learning vs. From-Scratch Training
DOI:
https://doi.org/10.30871/jaic.v9i4.9718Keywords:
Traffic Sign Detection, Transfer Learning, YOLO v11, Deep Learning, Indonesian Traffic DatasetAbstract
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 ([email protected]), and mean Average Precision across IoU thresholds 0.5 to 0.95 ([email protected]: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.
Downloads
References
[1] M.-Y. Fu and Y.-S. Huang, “A survey of traffic sign recognition,” in 2010 International Conference on Wavelet Analysis and Pattern Recognition, Qingdao, China: IEEE, Jul. 2010, pp. 119–124. doi: 10.1109/ICWAPR.2010.5576425.
[2] T. Chaudhari, A. Wale, A. Joshi, and S. Sawant, “Traffic Sign Recognition Using Small-Scale Convolutional Neural Network,” SSRN Journal, 2020, doi: 10.2139/ssrn.3645805.
[3] R. Girshick, J. Donahue, T. Darrell, and J. Malik, “Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation,” in 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA: IEEE, Jun. 2014, pp. 580–587. doi: 10.1109/CVPR.2014.81.
[4] S. Ren, K. He, R. Girshick, and J. Sun, “Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks,” in Advances in Neural Information Processing Systems, C. Cortes, N. Lawrence, D. Lee, M. Sugiyama, and R. Garnett, Eds., Curran Associates, Inc., 2015. [Online]. Available: https://proceedings.neurips.cc/paper_files/paper/2015/file/14bfa6bb14875e45bba028a21ed38046-Paper.pdf
[5] W. Liu et al., “SSD: Single Shot MultiBox Detector,” in Computer Vision – ECCV 2016, vol. 9905, B. Leibe, J. Matas, N. Sebe, and M. Welling, Eds., in Lecture Notes in Computer Science, vol. 9905. , Cham: Springer International Publishing, 2016, pp. 21–37. doi: 10.1007/978-3-319-46448-0_2.
[6] J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You Only Look Once: Unified, Real-Time Object Detection,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA: IEEE, Jun. 2016, pp. 779–788. doi: 10.1109/CVPR.2016.91.
[7] G. Jocher, J. Qiu, and A. Chaurasia, Ultralytics YOLO. (Jan. 10, 2023). Ultralytics. [Online]. Available: https://github.com/ultralytics/ultralytics
[8] S. Houben, J. Stallkamp, J. Salmen, M. Schlipsing, and C. Igel, “Detection of traffic signs in real-world images: The German traffic sign detection benchmark,” in The 2013 International Joint Conference on Neural Networks (IJCNN), Dallas, TX, USA: IEEE, Aug. 2013, pp. 1–8. doi: 10.1109/IJCNN.2013.6706807.
[9] A. Mogelmose, M. M. Trivedi, and T. B. Moeslund, “Vision-Based Traffic Sign Detection and Analysis for Intelligent Driver Assistance Systems: Perspectives and Survey,” IEEE Trans. Intell. Transport. Syst., vol. 13, no. 4, pp. 1484–1497, Dec. 2012, doi: 10.1109/TITS.2012.2209421.
[10] D. Temel, M.-H. Chen, and G. AlRegib, “Traffic Sign Detection Under Challenging Conditions: A Deeper Look into Performance Variations and Spectral Characteristics,” IEEE Trans. Intell. Transport. Syst., vol. 21, no. 9, pp. 3663–3673, Sep. 2020, doi: 10.1109/TITS.2019.2931429.
[11] F. Zhuang et al., “A Comprehensive Survey on Transfer Learning,” Proc. IEEE, vol. 109, no. 1, pp. 43–76, Jan. 2021, doi: 10.1109/JPROC.2020.3004555.
[12] T.-Y. Lin et al., “Microsoft COCO: Common Objects in Context,” 2014, arXiv. doi: 10.48550/ARXIV.1405.0312.
[13] J. Deng, W. Dong, R. Socher, L.-J. Li, Kai Li, and Li Fei-Fei, “ImageNet: A large-scale hierarchical image database,” in 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL: IEEE, Jun. 2009, pp. 248–255. doi: 10.1109/CVPR.2009.5206848.
[14] H.-C. Shin et al., “Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning,” IEEE Trans. Med. Imaging, vol. 35, no. 5, pp. 1285–1298, May 2016, doi: 10.1109/TMI.2016.2528162.
[15] C. Rahmad, I. F. Rahmah, R. A. Asmara, and S. Adhisuwignjo, “Indonesian traffic sign detection and recognition using color and texture feature extraction and SVM classifier,” in 2018 International Conference on Information and Communications Technology (ICOIACT), Yogyakarta: IEEE, Mar. 2018, pp. 50–55. doi: 10.1109/ICOIACT.2018.8350804.
[16] A. I. Pradana, S. Rustad, G. F. Shidik, and H. Agus Santoso, “Indonesian Traffic Signs Recognition Using Convolutional Neural Network,” in 2022 International Seminar on Application for Technology of Information and Communication (iSemantic), Semarang, Indonesia: IEEE, Sep. 2022, pp. 426–430. doi: 10.1109/iSemantic55962.2022.9920448.
[17] P. Chyan, N. T. S. Saptadi, and J. M. Leda, “Small Object Detection Approach Based On Enhanced Single-Shot Detector For Detection And Recognition Of Indonesian Traffic Signs,” BAREKENG: J. Math. & App., vol. 18, no. 4, pp. 2653–2662, Oct. 2024, doi: 10.30598/barekengvol18iss4pp2653-2662.
[18] 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,” in 2020 3rd International Seminar on Research of Information Technology and Intelligent Systems (ISRITI), Yogyakarta, Indonesia: IEEE, Dec. 2020, pp. 520–524. doi: 10.1109/ISRITI51436.2020.9315368.
[19] E. Yohannes et al., “Indonesia Traffic Sign Recognition Using a One-Stage Detector YOLOv8,” in 2024 Seventh International Conference on Vocational Education and Electrical Engineering (ICVEE), Malang, Indonesia: IEEE, Oct. 2024, pp. 169–174. doi: 10.1109/ICVEE63912.2024.10824005.
[20] Indonesia Traffic Sign, “Traffic Sign Dataset,” Roboflow Universe. Roboflow, Apr. 2024. [Online]. Available: https://universe.roboflow.com/indonesia-traffic-sign/traffic-sign-kdnr1
[21] S. J. Pan and Q. Yang, “A Survey on Transfer Learning,” IEEE Trans. Knowl. Data Eng., vol. 22, no. 10, pp. 1345–1359, Oct. 2010, doi: 10.1109/TKDE.2009.191.
[22] M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman, “The Pascal Visual Object Classes (VOC) Challenge,” Int J Comput Vis, vol. 88, no. 2, pp. 303–338, Jun. 2010, doi: 10.1007/s11263-009-0275-4.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Ibnu Ramadhan, Akhmad Hendriawan, Hary Oktavianto
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) ) that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
