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TRAFİK İŞARETLERİNİN TESPİTİNDE FARKLI YOLO MODELLERİNİN KARŞILAŞTIRILMASI

Yıl 2025, Cilt: 28 Sayı: 1, 138 - 150, 03.03.2025

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

Öz

Trafik işaretleri, karayolunda seyahat eden sürücülere yol kısıtlamaları açısından uyarıda bulunmak amacıyla karayollarına yerleştirilmektedir. Bu işaretlerin doğru bir şekilde algılanması ve trafik işaretinin gerektirdiği kısıtlamaya uyulması, sürüş güvenliği açısından önemlidir. Son yıllarda, derin öğrenme algoritmalarının nesne sınıflandırılmasında ve tespitinde başarılı olduğu birçok çalışma ile gösterilmiştir. Bu çalışmada, derin öğrenme tabanlı “You Only Look Once” (YOLO) algoritmaları trafik işaretleri tespiti açısından karşılaştırılmıştır. İlk olarak 877 görüntüden oluşan dört sınıflı trafik işaretleri veri seti elde edilmiştir. Daha sonra YOLOv5, YOLOv8 ve YOLOv9 algoritmaları trafik işareti tanıması açısından incelenmiştir. Deneysel çalışmalarda, tespit algoritmalarının performanslarını değerlendirmek amacıyla duyarlılık, kesinlik, f1 skor ve mAP performans değerlendirme kriterleri açısından incelenmiştir. Elde edilen deneysel sonuçlara göre YOLOv9’un Duyarlılık metriği %90.8, mAP@0.5 metriği %93.1 ve mAP@0.5:0.95 metriği %77.7 olarak hesaplanmıştır. Bu sonuçlar YOLOv9 algoritmasının en iyi trafik işareti tespit algoritması olduğunu doğrulamaktadır.

Anahtar Kelimeler

Derin Öğrenme YOLO Trafik İşaret Tespiti

Kaynakça

  • 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

THE COMPARISON OF DIFFERENT YOLO MODELS FOR TRAFFIC SIGN DETECTION

Yıl 2025, Cilt: 28 Sayı: 1, 138 - 150, 03.03.2025

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

Öz

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.

Anahtar Kelimeler

Deep Learning YOLO Traffic Sign Detection

Kaynakça

  • 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
Toplam 26 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Derin Öğrenme
Bölüm Bilgisayar Mühendisliği
Yazarlar

Mehmet Fatih Bekçioğulları 0000-0002-0056-9526

Bünyamin Dikici 0000-0001-6722-5894

Hakan Açıkgöz 0000-0002-6432-7243

Serkan Özbay 0000-0001-5973-8243

Yayımlanma Tarihi 3 Mart 2025
Gönderilme Tarihi 29 Temmuz 2024
Kabul Tarihi 2 Aralık 2024
Yayımlandığı Sayı Yıl 2025Cilt: 28 Sayı: 1

Kaynak Göster

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.
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