TRAFİK İŞARETLERİNİN TESPİTİNDE FARKLI YOLO MODELLERİNİN KARŞILAŞTIRILMASI

Mehmet Fatih Bekçioğulları; Bünyamin Dikici; Hakan Açıkgöz; Serkan Özbay

doi:10.17780/ksujes.1524094

EN TR

THE COMPARISON OF DIFFERENT YOLO MODELS FOR TRAFFIC SIGN DETECTION

Abstract

Traffic signs are placed on highways to warn drivers travelling on the highway in terms of road restrictions. It is important for driving safety that these signs are correctly detected and that the restrictions required by the traffic sign are obeyed. In recent years, many studies have shown that deep learning algorithms are successful in object classification and detection. In this study, deep learning based ‘You Only Look Once’ (YOLO) algorithms are compared in terms of traffic sign detection. Firstly, a four-class traffic sign dataset consisting of 877 images is collected. Then, YOLOv5, YOLOv8 and YOLOv9 algorithms are analyzed in terms of traffic sign recognition. In the experimental studies, in order to evaluate the performance of the detection algorithms, recall, precision, f1 score and mAP performance evaluation criteria are analyzed. According to the experimental results obtained, the Recall metric of YOLOv9 is 90.8%, mAP@0.5 metric is 93.1% , and mAP@0.5:0.95 metric is 77.7%. These results validate that YOLOv9 is the best traffic sign detection algorithm.

Keywords

References

Acikgoz, H. (2024). An automatic detection model for cracks in photovoltaic cells based on electroluminescence imaging using improved YOLOv7. Signal, Image and Video Processing, 18(1), 625-635. https://doi.org/10.1007/s11760-023-02724-7
Aykılıç, Ö., Başarslan, M. S., & Bal, F. (2024). Classification of Traffic Signs Using Transfer Learning Methods. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi, 24(4), 829-838. https://doi.org/10.35414/akufemubid.1420978
Bochkovskiy, A., Wang, C.-Y., & Liao, H. M. (2020). YOLOv4: Optimal Speed and Accuracy of Object Detection. http://arxiv.org/abs/2004.10934
Chen, Y., & Luo, H. (2024). VisioSignNet: A Dual-Interactive Neural Network for enhanced traffic sign detection. Expert Systems with Applications, 255. https://doi.org/10.1016/j.eswa.2024.124688
Çetinkaya, M., & Acarman, T. (2020). Trafik İşaret Levhası Tespiti için Derin Öğrenme Yöntemi. Akıllı Ulaşım Sistemleri Ve Uygulamaları Dergisi, 3(2), 140-157.
Çınarer, G. (2024). Deep Learning Based Traffic Sign Recognition Using YOLO Algorithm. Düzce Üniversitesi Bilim ve Teknoloji Dergisi, 12(1), 219-229. https://doi.org/10.29130/dubited.1214901
Ge, Z., Liu, S., Wang, F., Li, Z., & Sun, J. (2021). YOLOX: Exceeding YOLO Series in 2021. 1-7. http://arxiv.org/abs/2107.08430
Girshick, R. (2015). Fast R-CNN. Proceedings of the IEEE International Conference on Computer Vision, 2015 Inter, 1440-1448. https://doi.org/10.1109/ICCV.2015.169

Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 580-587. https://doi.org/10.1109/CVPR.2014.81
Han, Y., Wang, F., Wang, W., Zhang, X., & Li, X. (2024). EDN-YOLO: Multi-scale traffic sign detection method in complex scenes. Digital Signal Processing: A Review Journal, 153. https://doi.org/10.1016/j.dsp.2024.104615
Krizhevsky, A., Sutskever, I., & Hinton, G.E. (2012). ImageNet classification with deep convolutional neural networks. Communications of the ACM, 60(6), 84 - 90. https://doi.org/10.1145/3065386
Küçük, Ö., Yavşan, E., & Gökçe, B. (2021). Otonom Tabanlı İşaret ve Şerit Tanımak Amacı ile Bir Öğrenme Sisteminin Geliştirilmesi. International Journal of Engineering Research and Development, 13(3), 19-25. https://doi.org/10.29137/umagd.1037237
Li, C., Li, L., Jiang, H., Weng, K., Geng, Y., Li, L., Ke, Z., Li, Q., Cheng, M., Nie, W., Li, Y., Zhang, B., Liang, Y., Zhou, L., Xu, X., Chu, X., Wei, X., & Wei, X. (2022). YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications. http://arxiv.org/abs/2209.02976
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C. Y., & Berg, A. C. (2016). SSD: Single shot multibox detector. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9905 LNCS, 21-37. https://doi.org/10.1007/978-3-319-46448-0_2
Megalingam, R. K., Thanigundala, K., Musani, S. R., Nidamanuru, H., & Gadde, L. (2023). Indian traffic sign detection and recognition using deep learning. International Journal of Transportation Science and Technology, 12(3), 683-699. https://doi.org/10.1016/j.ijtst.2022.06.002
Rani, A. R., Anusha, Y., Cherishama, S. K., & Laxmi, S. V. (2024). Traffic sign detection and recognition using deep learning-based approach with haze removal for autonomous vehicle navigation. e-Prime - Advances in Electrical Engineering, Electronics and Energy, 7. https://doi.org/10.1016/j.prime.2024.100442
Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You only look once: Unified, real-time object detection. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016-Decem, 779-788. https://doi.org/10.1109/CVPR.2016.91
Redmon, J., & Farhadi, A. (2017). YOLO9000: Better, faster, stronger. Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, 2017-Janua, 6517-6525. https://doi.org/10.1109/CVPR.2017.690
Redmon, J., & Farhadi, A. (2018). YOLOv3: An Incremental Improvement. http://arxiv.org/abs/1804.02767
Ren, S., He, K., Girshick, R., & Sun, J. (2017). Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(6), 1137-1149. https://doi.org/10.1109/TPAMI.2016.2577031
Saxena, S., Dey, S., Shah, M., & Gupta, S. (2024). Traffic sign detection in unconstrained environment using improved YOLOv4. Expert Systems with Applications (C. 238). Elsevier Ltd. https://doi.org/10.1016/j.eswa.2023.121836
Terven, J., Córdova-Esparza, D. M., & Romero-González, J. A. (2023). A Comprehensive Review of YOLO Architectures in Computer Vision: From YOLOv1 to YOLOv8 and YOLO-NAS. Machine Learning and Knowledge Extraction (C. 5, Sayı 4, ss. 1680-1716). Multidisciplinary Digital Publishing Institute (MDPI). https://doi.org/10.3390/make5040083
Trappey, A. J. C., & Shen, O. T. C. (2024). A universal traffic sign detection system using a novel self-training neural network modeling approach. Advanced Engineering Informatics, 62. https://doi.org/10.1016/j.aei.2024.102674
Wang, A., Chen, H., Liu, L., Chen, K., Lin, Z., Han, J., & Ding, G. (2024). YOLOv10: Real-Time End-to-End Object Detection. http://arxiv.org/abs/2405.14458
Wang, C.-Y., Bochkovskiy, A., & Liao, H.-Y. M. (2023). YOLOv7: Trainable Bag-of-Freebies Sets New State-of-the-Art for Real-Time Object Detectors. 7464-7475. https://doi.org/10.1109/cvpr52729.2023.00721
Wang, C.-Y., Yeh, I.-H., & Liao, H.-Y. M. (2024). YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information. http://arxiv.org/abs/2402.13616

Details

Primary Language

Turkish

Subjects

Deep Learning

Journal Section

Research Article

Authors

Mehmet Fatih Bekçioğulları ^*
0000-0002-0056-9526
Türkiye

Bünyamin Dikici
0000-0001-6722-5894
Türkiye

Hakan Açıkgöz
0000-0002-6432-7243
Türkiye

Serkan Özbay
0000-0001-5973-8243
Türkiye

Publication Date

March 3, 2025

Submission Date

July 29, 2024

Acceptance Date

December 2, 2024

Published in Issue

Year 1970 Volume: 28 Number: 1

DOI

https://doi.org/10.17780/ksujes.1524094

IZ

https://izlik.org/JA59XA28FC

Cite

RIS / Bibtex

APA

Bekçioğulları, M. F., Dikici, B., Açıkgöz, H., & Özbay, S. (2025). TRAFİK İŞARETLERİNİN TESPİTİNDE FARKLI YOLO MODELLERİNİN KARŞILAŞTIRILMASI. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 28(1), 138-150. https://doi.org/10.17780/ksujes.1524094

Cited By

Helmet Detection System with YOLO Architecture in Real-Time Construction Site Cameras

Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi

https://doi.org/10.54365/adyumbd.1757010

THE COMPARISON OF DIFFERENT YOLO MODELS FOR TRAFFIC SIGN DETECTION

Abstract

Keywords