Vehicle type classification with deep learning
Yaraş, Neriman, author.

Vehicle type classification with deep learning

Yaraş, Neriman, author.

Yazar Ek Girişi
Yaraş, Neriman, author.

Fiziksel Tanımlama
viii, 68 leaves: color illustrarions, charts;+ 1 computer laser optical disc.

In this thesis, we studied the vehicle type classification problem from several perspectives. We apply a deep learning technique with different parameters such as image size and the number of images in data sets to the classification of an image as one of the nine vehicle types. After choosing the most appropriate one among trained models, we convert the problem into a hierarchical tree classification problem so that it could be analyzed in three different tree hierarchies. Experiments are performed using three computational methods for calculating possibilities for each of the nine classes that correspond to the leaves of the hierarchical trees. These studies result in a conclusion that 0.762812 average accuracy is obtained when traditional arithmetic mean computation applied on the hierarchical tree with level-2 using the Stanford Dataset by 224 image size on ResNet34 architecture.

Konu Başlığı
Neural networks (Computer science)
Machine learning.
Artificial intelligence.

Yazar Ek Girişi
Özuysal, Mustafa,

Tüzel Kişi Ek Girişi
İzmir Institute of Technology. Computer Engineering.

Tek Biçim Eser Adı
Thesis (Master)--İzmir Institute of Technology: Computer Engineering.
İzmir Institute of Technology: Computer Engineering--Thesis (Master).

Elektronik Erişim
Access to Electronic Versiyon.

KütüphaneMateryal TürüDemirbaş NumarasıYer NumarasıDurumu/İade Tarihi
IYTETezT002205QA76.87 .Y26 2020Tez Koleksiyonu