Çok Markalı Servis İstasyonları için Yapay Sinir Ağları ile Görüntü Tabanlı Araç Marka ve Modeli Tanıma Yazılımı

Bu çalıșmada çok markalı araç servis istasyonları için görüntü tabanlı araç marka ve modeli tanıma uygulaması geliștirilmiștir. Akıllı otoyol sistemleri için geliștirilmiș uygulamalardan farklı olarak bu uygulamada tablet pc gibi düșük performanslı sistemlerde ve farklı ișletim sistemleri yüklü kișisel bilgisayarda hızlı ve etkili bir biçimde çalıșabilecek bir araç tanıma sistemi geliștirilmesi amaçlanmıștır. Bu nedenle uygulama Java ile geliștirilmiștir. Ağı eğitirken iyi bir eğitim setinin kullanılması önemlidir. Test sonuçlarından elde edilen verilere göre % 99’un üstünde bașarı elde edilmiștir

A Vision Based Car Brand and Model Recognition Software with Artificial Neural Networks for Multi-Brand Service Stations

In this study, a vision based vehicle type recognition software for multi-brand service stations has been developed. Different from the applications developed for intelligent motorway systems, in this application it is aimed to develop a vehicle recognition system to run on low performance systems like tablet PCs and PCs with different operating systems. So the application has been developed with Java. It is important to use a good training set while training the network. According to the data acquired from the test results, a success over % 99 has been attained

Keywords:

Vehicle Recognition, ANN, Image Processing, Prewitt Operator Back Propagation Algorithm,

PDF

___

[1] Zhang X., Jiang L., Vehicle Types Recognition Based on Neural Network, Proceedings, CINC '09. International Conference on Computational Intelligence and Natural Computing, 1, 3-6, (2009). [2] Wang, W., Vehicle Type Recognition Based on Radial Basis Function Neural Networks, Proceedings, JCAI '09. International Joint Conference on Artificial Intelligence, 444-447, (2009). [3] Wang, W., A Study on Contour Feature Algorithm for Vehicle Type Recognition, Proceedings, JCAI '09. International Joint Conference on Artificial Intelligence, 452-455, (2009). [4] Wu, W., Qisen Z., Mingjun, W., A method of vehicle classification using models and neural networks, Proceedings, JCAI '09. International Joint Conference on Artificial Intelligence, 4, 3022-3026, (2001). [5] Lan, L.W., Kuo, A.Y., Development of a fuzzy neural network color image vehicular detection, Proceedings, The IEEE 5th International Conference on Intelligent Transportation Systems, 88-93, (2002). [6] Haykin S., Neural Networks, 2nd ed., Pearson Education, New Jersey, (1999). [7] Liu, J., Sun, J., and Wang, S., Pattern Recognition: An Overview, Proceedings, IJCSNS International Journal of Computer Science and Network Security, 6, (2006). [8] Prewitt, http://en.wikipedia.org/wiki/Prewitt (4.5.2009).