Development of Artificial Intelligence Based Vehicle Detection System

Yıl 2020, Cilt: 1 Sayı: 1, 31 - 37, 20.07.2020

Şenol Pazar , Mehmet Bulut , Cihan Uysal

https://doi.org/10.5281/zenodo.3922425

Öz

Abstract - Vehicle detection and traffic conditions are important factors for safe driving, accident avoidance, automatic driving and tracking. Currently, Traffic Signal Controller (TSD)systems are used at intersections for traffic signalization operations. Traffic Signal Loop Detectors installed under the road are used in these systems. The installation, operation and maintenance of these detectors are time consuming and costly. Loop detectors can also be damaged if there is any damage on the road. In addition, traffic road information that can be obtained with detectors is also very limited. In this study, which brought a new solution to traffic signalization in many cities in the transportation sector, a vehicle detection system (Video Detector) was developed with an Artificial Intelligence based camera. The main purpose of the study is to develop an image based sensor system as an alternative to conventional under-asphalt magnetic and electrical vehicle sensors. Thanks to the system developed with this study, the need for costly physical loop detectors can be avoided. First phase of this study, vehicle presence and counting can be possible by virtual sensors. Later, vehicle tracking information can be generated depending onvehicle occupation, queuing, average speed, vehicle classification at intersections.

Anahtar Kelimeler

Vehicle Detection, Artifical Intelligence, Intelligent Transportation Systems, Artificial Intelligence

Yapay Zeka Tabanlı Araç Algılama Sistemi Geliştirilmesi

Öz

Araç algılama ve trafik durumları güvenli sürüş, kazadan kaçınma, otomatik sürüş ve takip için önemli unsurlardır. Halihazırda, trafik sinyalizasyon işlemleri için kavşaklarda Trafik Sinyal Denetleyicisi (TSD) sistemleri kullanılmaktadır. Bu sistemlerde yol altına döşenen Trafik Sinyal Loop Dedektörleri bulunmaktadır. Bu dedektörlerin kurulumu, işletilmesi ve bakımı, zaman alıcı ve maliyetli işlemlerdir. Yolda bir hasar olması durumunda, loop dedektörler de hasar görebilmektedir. Ayrıca dedektörlerle alınabilecek trafik yol bilgisi de oldukça sınırlı kalmaktadır. Ulaşım sektöründe birçok şehirdeki trafik sinyalizasyonuna yeni bir çözüm getirecek bu proje çalışmasında, yapay zeka tabanlı kamera ile bir araç algılama sistemi (Video Dedektör) geliştirilmiştir. Projenin temel amacı geleneksel asfalt altı manyetik ve elektriksel araç algılayıcılara alternatif olarak, görüntü tabanlı algılayıcı bir sistem geliştirilmesidir. Bu projeyle geliştirilen sistem sayesinde, maliyetli fiziksel loop dedektörlerine olan ihtiyacın kalkması hedeflenmektedir. Bu çalışma kapsamında yapay zeka temelli çalışan bir video algılayıcı sistem kullanılarak; ilk aşamada sanal loop, araç sayım, varlık-yokluk bilgisi, sonraki aşamalarda ise araç işgaliye, kuyruklanma, ortalama hız, araç sınıflandırma ve kavşak içi yönlere bağlı olarak araç takibi bilgileri üretilebilecektir.

Anahtar Kelimeler

Araç Algılama, Trafik Sinyalizasyon, Yapay Zeka, Akıllı Ulaşım Sistemleri

Kaynakça

• [1] J. Huang, V. Rathod, C. Sun, M. Zhu, A. Korattikara, A. Fathi, I. Fischer, Z. Wojna, Y. Song, S. Guadarrama, K. Murphy, (2017), Speed/Accuracy Trade-Offs for Modern Convolutional Object Detectors,The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 7310-7311.
• [2] Anthony Sarkis, (2017), Self-Driving Cars: Implementing Real-Time Traffic Light Detection and Classification, https://medium.com/@anthony_sarki.
• [3] NVIDIA TensorRT, (2018), https://developer.nvidia.com/tensorrt
• [4] Sakshi Indolia et all., (2018), Conceptual Understanding Of Convolutional Neural Network- A Deep Learning Approach,Procedia Computer Science 132, pp. 679–688.
• [5] Vijayasanthi D., Geetha S., (2017), Deep Learnıng Approach Model For Vehıcle Classıfıcatıon Usıng Artıfıcıal Neural Network, International Research Journal of Engineering and Technology (IRJET) , Volume: 04 -06, pp. 1418-1424.
• [6] Surendra Gupte et all., (2002), Detection And Classification Of Vehicles, IEEE Transactions On Intelligent Transportation Systems, Vol. 3, No. 1
• [7] Jiyong Chung and Keemin Sohn, (2018), Image-Based Learning to Measure Traffic Density Using a Deep Convolutional Neural Network, IEEE Transactions On Intelligent Transportation Systems, Vol. 19, No. 5
• [8] Sayanan Sivaraman, Mohan Manubhai Trivedi, (2013), Looking at Vehicles on the Road: A Survey of Vision-Based Vehicle Detection, Tracking and Behavior Analysi, IEEE Transactions on Intelligent Transportation Systems, Vol. 14, No. 4
• [9] Abhijeet Kumar, Gunshi Gupta, Avinash Sharma and K. Madhava Krishna, (2018), Towards View-Invariant Intersection Recognition from Videos using Deep Network Ensembles, Intelligent Robots and Systems (IROS), IEEE/RSJ International Conference on, pp. 1053-1060
• [10] Bhatt Dhaivat, Sodhi Danish, Pal Arghya, Balasubramanian Vineeth and Krishna Madhava, (2017),HaveI reached the intersection: A deep learning-based approach for intersection detection from monocular cameras. 4495-4500. 10.1109/IROS.2017.8206317
• [11] Arcos García, Álvaro et all., (2018), Evaluation of Deep Neural Networks for traffic sign detection systems. Neurocomputing. 316. 10.1016/j.neucom.2018.08.009
• [12] Shi Wenxu, Bao Shengli and Tan Dailun, (2019), FFESSD: An Accurate and Efficient Single-Shot Detector for Target Detection. Applied Sciences, 9. 4276. 10.3390/app9204276
• [13] Jiuxiang Gu et all., (2015), Recent Advances in Convolutional Neural Networks,Pattern Recognition,Vol. 77, pp. 354-377.
• [14] Ciresan Dan et all.,(2011), Flexible,High Performance Convolutional Neural Networks for Image Classification, Proceedings of the Twenty-SecondInternational Joint Conference on Artificial Intelligence-Vol.2, 1237–1242.
• [15] Neethidevan Veerapathiran and Chandrasekaran G., (2020), Image Segmentation for Object Detection using Mask R-CNN in Colab,GRD Journal for Engineering , Vol.5, 15-19