Kurkova, V.Manolopoulos, Y.Hammer, B.Iliadis, L.Maglogiannis, I.2023-11-102023-11-102018Satar, B. ve Dirik, A. E. (2018). ''Deep learning based vehicle make-model classification''. ed. K. Kurkova vd. Lecture Notes in Computer Science, Artificial Neural Networks and Machine Learning – ICANN 2018, 11141(Part III), 544-553.978-3-030-01424-70302-97431611-3349https://doi.org/10.1007/978-3-030-01424-7_53https://link.springer.com/chapter/10.1007/978-3-030-01424-7_53http://hdl.handle.net/11452/34844Bu çalışma, 04-07 Ekim 2018 tarihlerinde Rhodes[Yunanistan]’da düzenlenen 27. International Conference on Artificial Neural Networks (ICANN) Kongresi‘nde bildiri olarak sunulmuştur.This paper studies the problem of vehicle make & model classification. Some of the main challenges are reaching high classification accuracy and reducing the annotation time of the images. To address these problems, we have created a fine-grained database using online vehicle marketplaces of Turkey. A pipeline is proposed to combine an SSD (Single Shot Multibox Detector) model with a CNN (Convolutional Neural Network) model to train on the database. In the pipeline, we first detect the vehicles by following an algorithm which reduces the time for annotation. Then, we feed them into the CNN model. It is reached approximately 4% better classification accuracy result than using a conventional CNN model. Next, we propose to use the detected vehicles as ground truth bounding box (GTBB) of the images and feed them into an SSD model in another pipeline. At this stage, it is reached reasonable classification accuracy result without using perfectly shaped GTBB. Lastly, an application is implemented in a use case by using our proposed pipelines which detects the unauthorized vehicles by comparing their license plate numbers and make & models. It is assumed that license plates are readable.eninfo:eu-repo/semantics/openAccessComputer scienceDeep learningVehicleModelClassificationCNNResNetDetectionSSDFraudLicense plateClassification (of information)Error detectionLicense plates (automobile)ModelsNeural networksPipelinesVehiclesBounding boxClassification accuracyConvolutional neural networkFine grainedFraudModel classificationDeep learningSingle shotsProceedings Paper0004633400000532-s2.0-8505480149054455311141Part IIIComputer science, artificial intelligenceObject Detection; Deep Learning; IOU