Classification of Vehicle Types Using İMobileNet CNN Approaches and Feature Selection Methods

Abstract

Nowadays, the density of vehicles in traffic life has reached serious levels. Therefore, the utilization capacity of existing transportation networks increases to maximum levels and leads to traffic congestion. Visual Traffic Surveillance Systems, a solution of Intelligent Transportation Systems, is one of the alternative method used to reduce traffic congestion. One of the main tasks of the Visual Traffic Surveillance System is to correctly classify the types of vehicles detected from video or images. This study aims to present new methods that will improve the accuracy of the visual Traffic Surveillance System in classifying vehicle types. While traditional methods are used in most studies that increase image classification accuracy, today's trend mobile convolutional neural networks (MCNN) are considered with two different approaches in this study. Firstly, the MobileNetv1 and MobileNetv2 models were optimized and the İMobileNetv1 and İMobileNetv2 approaches were proposed. Second, the proposed MCNN approaches were used only as feature extractors. An approach that uses methods such as combining, selecting, and classifying the features obtained from these approaches was proposed. As a result of the classification made with the proposed approaches, a very high classification success rate of 85.05% has been achieved.

Keywords

• Deep learning
• Feature extraction
• Feature selection
• Classification
• Vehicle Types

İMobileNet CNN Yaklaşımları ve Özellik Seçme Yöntemleri Kullanarak Araç Türlerini Sınıflandırma

Abstract

Günümüzde, trafik hayatında seyreden araç yoğunluğu ciddi boyutlara ulaşmıştır. Bu nedenle, mevcut ulaşım ağlarının kullanım kapasitesi maksimum seviyelere çıkmakta ve trafik sıkışıklığına yol açmaktadır. Akıllı Ulaşım Sistemlerinin bir çözümü olan Görsel Trafik Gözetleme Sistemleri trafik sıkışıklığını azaltmak için kullanılan alternatif yöntemlerden biridir. Görsel Trafik Gözetleme Sisteminin temel görevlerinden biri; video veya görüntülerden algılanan araç türlerini doğru bir şekilde sınıflandırmaktır. Bu çalışma, Görsel Trafik Gözetleme Sisteminin araç türlerini sınıflandırma doğruluğunu arttıracak yeni yöntemler sunmayı amaçlamaktadır. Çoğu görüntü sınıflandırma doğruluğunu arttıran çalışmalarda geleneksel yöntemler kullanılırken bu çalışmada günümüzde trend olan mobil evrişimli sinir ağları (MCNN) iki farklı yaklaşımla ele alınmaktadır. İlk olarak, MobileNetv1 ve MobileNetv2 modelleri optimize edilerek İMobileNetv1 ve İMobileNetv2 yaklaşımları önerildi. İkinci olarak, bu önerilen MCNN yaklaşımları sadece özellik çıkarıcı olarak kullanıldığı ve elde edilen özelliklerin birleştirilmesi, seçilmesi ve sınıflandırılması gibi yöntemlerin birlikte kullanıldığı bir yaklaşım önerildi. Önerilen yaklaşımlarla yapılan sınıflandırma sonucunda, %85,05 oranında çok yüksek bir sınıflandırma başarısı elde edilmiştir.

Keywords

• Derin öğrenme
• Özellik seçme
• Sınıflandırma
• Araç Türleri
• Özellik çıkarma

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