Derin Öğrenme ile Trafik İşareti Tanıma Algoritmasının Gerçek Zamanlı Uygulaması

Year 2022, , 275 - 289, 18.12.2022

Faruk Emre Aysal , Kasım Yıldırım , Enes Cengiz

https://doi.org/10.55546/jmm.1196409

Cited By: 3

Abstract

Otonom taşıtlar, otomotiv teknolojisinde popülaritesi giderek artan uygulama alanlardan biridir. Bu taşıtlar, iletişim, koordinasyon ve otonom sürüş yeteneğine sahip olmasıyla, ulaşım sistemlerini iyileştirmede önemli potansiyeller göstermektedir. İnsan müdahalesi olmadan kaynaktan hedefe hareket eden bu taşıtlar, kazalar ve trafik sıkışıklığı gibi insanların trafikte neden olduğu çeşitli sorunlara çözüm olarak ortaya çıktığı görülmektedir. Trafik kazaları ve trafik sıkışıklıkları büyük oranda sürücü kusurlarından ve trafik kurallarına uyulmamasından kaynaklanmaktadır. Bu nedenle, otonom taşıtlara yapay zekâ (AI) tabanlı sistemlerin entegre edilmesinin sosyal hayatta problem olarak görülen bu gibi durumlara çözüm olacağı öngörülmektedir. Literatüre bakıldığında VGGNet, ResNet50, MobileNetV2, NASNetMobile, İleri Beslemeli Sinir Ağları (Feed Forward Neural Network), Yinelemeli Sinir Ağları (Recurrent Neural network), Uzun Kısa Süreli Bellek (Long-Short Term Memory) ve Kapı Yinelemeli Birimler (Gated Recurrent Units) gibi derin öğrenme modellerinin yaygın bir şekilde trafik işareti sınıflandırma çalışmalarında kullanıldığı görülmektedir. Önceki araştırmalardan farklı olarak bu çalışmada, açık kaynak bir veri seti ile YOLOv5 versiyonlarının modelleri kullanılarak trafik işaret ve işaretçilerinin algılanması üzerine bir derin öğrenme uygulaması yapılmıştır. Özgün veri seti hazırlanarak çalışmada kullanılmıştır. Bu veri setinin farklı AI modellerine uygun olarak etiketleme işlemi tamamlanmıştır. Geliştirilen CNN modellerinde 15 farklı trafik işaret levha sınıfını içeren veri setinin eğitim işlemi gerçekleştirilmiştir. Bu modellerin sonuçları sistematik olarak karşılaştırılmış ve hiper parametre değişiklikleri ile modellerden optimum performans değerleri elde edilmiştir. Performans sonuçları incelendiğinde her bir sınıf için %98-99 oranında tespit başarısı elde edilmiştir.

Keywords

Derin Öğrenme, Otonom Taşıt, Gerçek Zamanlı Uygulama, YOLOv5

Real-Time Application of Traffic Sign Recognition Algorithm with Deep Learning

Abstract

Autonomous vehicles are one of the increasingly widespread application areas in automotive technology. These vehicles show significant potential in improving transportation systems, with their ability to communicate, coordinate and drive autonomously. These vehicles, which move from source to destination without human intervention, appear to be a solution to various problems caused by people in traffic, such as accidents and traffic jams. Traffic accidents and traffic jams are largely due to driver faults and non-compliance with traffic rules. For this reason, it is predicted that integrating artificial intelligence (AI)-based systems into autonomous vehicles will be a solution to such situations, which are seen as a problem in social life. Looking at the literature, VGGNet, ResNet50, MobileNetV2, NASNetMobile, Feed Forward Neural Networks, Recurrent Neural Networks, Long-Short Term Memory, and Gate Recurrent Units It is seen that deep learning models such as these are widely used in traffic sign classification studies. Unlike previous studies, in this study, a deep learning application was made for the detection of traffic signs and markers using an open-source data set and models of YOLOv5 versions. The original data set was prepared and used in the study. Labeling of this data set in accordance with different AI models has been completed. In the developed CNN models, the training process of the data set containing 15 different traffic sign classes was carried out. The results of these models were systematically compared, and optimum performance values were obtained from the models with hyperparameter changes. Real-time application was made using the YOLOv5s model. As a result, a success rate of 98-99% was achieved.

Keywords

Deep Learning, Autonomous Vehicle, Real-Time Application, YOLOv5

References

• Bayram F., Derin öğrenme tabanlı otomatik plaka tanıma. Politeknik Dergisi 23(4), 955-960, 2020.
• Çetin E., Ortataş F., Elektrikli ve Otonom Araçlarda Makine Öğrenmesi Kullanarak Trafik Levhaları Tanıma ve Simülasyon Uygulaması. El-Cezeri 8(3), 1081-1092, 2021.
• Dorokhin S., Artemov A., Likhachev D., Novikov, A., Starkov E., Traffic simulation: an analytical review. IOP Conference Series: Materials Science and Engineering, 918, 2020.
• Eraqi H. M., Moustafa M. N., Honer J., End-to-End Deep Learning for Steering Autonomous Vehicles Considering Temporal Dependencies. 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA., 2017.
• Fagnant D. J., Kockelman K., Preparing a nation for autonomous vehicles: opportunities, barriers and policy recommendations. Transportation Research Part A: Policy and Practice 77, 167-181, 2015.
• Glikson E., Woolley A. W., Human Trust in Artificial Intelligence: Review of Empirical Research. Academy of Management Annals 14(2), 627-660, 2020.
• Gopalakrishnan S., A Public Health Perspective of Road Traffic Accidents. Journal of Family Medicine Primary Care 1(2), 144-150, 2012.
• Guo Y., Liu Y., Oerlemans A., Lao S., Wu S., Lew M., Deep learning for visual understanding: A review. Neurocomputing 187, 27-48. 2016.
• Haenlein M., Kaplan A., A Brief History of Artificial Intelligence: On the Past, Present, and Future of Artificial Intelligence. California Management Review 61(4), 5-14, 2019.
• Hasan R. I., Yusuf S. M., Alzubaidi L., Review of the State of the Art of Deep Learning for Plant Diseases: A Broad Analysis and Discussion, Plants 9(10), 1302, 2020.
• Huval B., Wang T., Tandon S., Kiske J., Song W., Pazhayampallil J., Ng A. Y., An Empirical Evaluation of Deep Learning on Highway Driving. Computer Science, 2015.
• Jain N. K., Saini R. K., Mittal P., A Review on Traffic Monitoring System Techniques, Soft Computing: Theories and Applications, 569-577, 2018.
• Kour A., Yadav V. K., Maheshwari V., Prashar D., A Review on Image Processing. International Journal of Electronics Communication and Computer Engineering 4(1), 270-275, 2013.
• Kulkarni R., Dhavalikar S., Bangar S., Traffic Light Detection and Recognition for Self Driving Cars Using Deep Learning, Fourth International Conference on Computing Communication Control and Automation (ICCUBEA), Pune/India, August 16-18, 2018.
• Kumar G., Bhatia P. K., A Detailed Review of Feature Extraction in Image Processing Systems, Fourth International Conference on Advanced Computing & Communication Technologies, Rohtak/India, February 8-9, 2014, pp: 5-12.
• Lim K., Hong Y., Choi Y., Byun H., Real-time traffic sign recognition based on a general purpose GPU and deep-learning. PLOS ONE, 2017.
• Miglani A., Kumar N., Deep learning models for traffic flow prediction in autonomous vehicles: A review, solutions, and challenges. Vehicular Communications 20, 100184, 2019.
• Narang P; Agarwal A, Sanu A. S., Detecting subtle intraocular movements: Enhanced frames per second recording (slow motion) using smartphones. Journal of Cataract & Refractive Surgery 41(6), 1321-1323, 2015.
• Palandız T., Bayrakçı H. C., Yapay Zekâ Kullanılarak Trafik İşaret Levhalarının Sınıflandırılması: Denizli İl Merkezi İçin Örnek Bir Uygulama. International Journal of 3D Printing Technologies and Digital Industry 5(3), 645-653, 2021.
• Sampedro C., Ramos A. R., Campoy P., A Review of Deep Learning Methods and Applications for Unmanned Aerial Vehicles, Journal of Sensors, 2017.
• Sarkar S. B., Mohan B. C., Review on Autonomous Vehicle Challenges. First International Conference on Artificial Intelligence and Cognitive Computing, 593-603, 2018.
• Satria R., Castro M., GIS Tools for Analyzing Accidents and Road Design: A Review. Transportation Research Procedia 18, 242-247, 2016.
• Shrestha A., Mahmood A., Review of Deep Learning Algorithms and Architectures, IEEE Access 7, 53040-53065, 2019.
• Swaminathan V., Arora S., Bansal R., Rajalakshmi R., Autonomous Driving System with Road Sign Recognition using Convolutional Neural Networks, International Conference on Computational Intelligence in Data Science (ICCIDS), Chennai/India, February 21-23, 2019.
• Web Site 1: “YOLOv5 New Version- Improvements and Evaluation”, Access date: 20/05/2022, https://blog.roboflow.com/YOLOv5-improvements-and-evaluation/
• Wiley V., Lucas T., Computer Vision and Image Processing: A Paper Review. International Journal of Artificial Intelegence Research 2(1), 28-36, 2018.
• Zhou L., Zhang C., Liu F., Qiu Z., He Y., Application of Deep Learning in Food: A Review, Comprehensive Reviews in Food Science and Food Safety18(6), 1793-1811, 2019.