Vehicle Position Estimation and Vehicle Classification Using Deep Convolutional Neural Networks

Bashaer Isam Hasan KABEAYLA; Yasa EKŞİOĞLU ÖZOK

doi:https://doi.org/10.53600/ajesa.891207

Makale detayları

Vehicle Position Estimation and Vehicle Classification Using Deep Convolutional Neural Networks

Yazarlar:

Bashaer Isam Hasan KABEAYLA | Yasa EKŞİOĞLU ÖZOK

Yayıncı:

Altınbaş University

Ana Başlıklar:

Veri işleme & bilgisayar bilimi

Dergi:

AURUM Journal of Engineering Systems and Architecture 2021 Vol.5 Issue 1, pp.11-28

Belge Tipi:

Makale

Yayın Tarihi:

6/2021

Belge Dili:

İngilizce

Document ID (Org No.):

2000908705

Sıklık:

İki Yıllık

pISSN:

2564-6397

DOI:

https://doi.org/10.53600/ajesa.891207

Özet:

 

The aim of this paper is to classify the vehicles and estimate the position with license plate localization using deep convolutional Neural Network (DCNN). Vehicle pose estimation with license plate localization serves as one of the most widely-used real-world applications in fields like toll control, traffic scene analysis, and suspected vehicle tracking. We proposed a one-stage anchor-free vehicle classifier for simultaneously localizing the region of license plates and vehicles’ poses. The classifier, rather than bounding rectangles, gives bounding quadrilaterals, which gives a more precise indication for vehicle pose estimation with license plates localization. For single scale input, we reached mean Precision Accuracy mAP/mAP50 of 35.4/82.3 on the LISA benchmark dataset, already outperformed the existing commercial systems OpenALPR and Sighthound. For multi-scale input, we reached the best mAP/mAP50 of 40.8/90.1. For the vehicle pose (front-rear), classification accuracy reached 98.8%, average IoU reached 71.3%, giving a promising result as an end-to-end vehicle position estimation and license plate localization with contextual information. The work has performed in python programming language with several libraries of deep learning were being used for this purpose. Our DCNN model training started from an initial weight which we had already trained for about 110000 iterations in the model without classification head, so the total training iterations will be around 780000 including the transfer learning part in DCNN. Transfer learning made the DCNN model start at a smart point and made it easier to optimize all of the functional heads simultaneously.

Makale detayları

Vehicle Position Estimation and Vehicle Classification Using Deep Convolutional Neural Networks

Yazarlar: Bashaer Isam Hasan KABEAYLA | Yasa EKŞİOĞLU ÖZOK

Yayıncı: Altınbaş University

Ana Başlıklar: Veri işleme & bilgisayar bilimi

Dergi: AURUM Journal of Engineering Systems and Architecture 2021 Vol.5 Issue 1, pp.11-28

Belge Tipi: Makale

Yayın Tarihi: 6/2021

Belge Dili: İngilizce

Document ID (Org No.): 2000908705

Sıklık: İki Yıllık

pISSN: 2564-6397

DOI: https://doi.org/10.53600/ajesa.891207

Sayı

Yazarlar:

Bashaer Isam Hasan KABEAYLA | Yasa EKŞİOĞLU ÖZOK

Yayıncı:

Altınbaş University

Ana Başlıklar:

Veri işleme & bilgisayar bilimi

Dergi:

AURUM Journal of Engineering Systems and Architecture 2021 Vol.5 Issue 1, pp.11-28

Belge Tipi:

Makale

Yayın Tarihi:

6/2021

Belge Dili:

İngilizce

Document ID (Org No.):

2000908705

Sıklık:

İki Yıllık

pISSN:

2564-6397

DOI:

https://doi.org/10.53600/ajesa.891207