尼日利亚道路上的车辆制造和模型识别的手工制作和转移学习特征技术

IF 0.8 Q3 MULTIDISCIPLINARY SCIENCES
Martins E. Irhebhude, None Adeola O Kolawole, None Michael Chinonye Izuegbu
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引用次数: 0

摘要

由于车辆类别繁多,且不同类别之间存在相似性,因此车辆品牌和模型识别(VMMR)是一项具有挑战性的任务。研究表明,作品对不同国家的品牌和车型都有识别能力。尼日利亚道路上流行的车辆可能包括以下产品;丰田、本田、标致、奔驰、英盛等。VMMR在智能交通系统中非常重要,因此,本文提出了一种手工制作的迁移学习模型,用于检测静止车辆并根据品牌、制造和型号对其进行分类。引入了一个新的数据集,由在尼日利亚道路上行驶的流行品牌车辆的选定图像组成。通过使用EfficientNet和HOG模型提取特征,开发了汽车品牌和车型识别框架,并在本地收集的数据集上进行了评估。采用线性支持机向量(SVM)进行分类。实验结果表明,HOG的准确率为94.5%,effentnet的准确率为97%,HOG和effentnet特征拼接的准确率为98.1%。所提出的连接模型通过提供更高的精度和具有最多分类图像数量的混淆矩阵,优于HOG和EfficientNet提取的特征。研究表明,该模型在识别汽车品牌和车型的准确性方面具有优势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Handcrafted and Transfer Learned Feature Techniques for Vehicle Make and Model Recognition on Nigerian Road
The vehicle makes and model recognition (VMMR) is a challenging task due to the wide range of vehicle categories and similarities between different classes. Studies have shown that works have recognized vehicles of different countries' make and models. Popular vehicles on Nigerian roads may include products like; Toyota, Honda, Peugeot, Benz, Innoson Vehicle Manufacturing (IVM), etc. The VMMR is important in the intelligent transport system hence, this paper presents a handcrafted and transfer learning model to detect stationary vehicles and classify them based on brand, make, and model. A new dataset was introduced consisting of selected images of popular brands of vehicles driven on Nigerian roads. Framework for a vehicle make and model recognition was developed by extracting features using EfficientNet and HOG models and evaluated on the locally gathered datasets. For classification, a linear Support Machine Vector (SVM) was used. Experimental results showed 94.5% on HOG, 97% with EfficientNet, and 98.1% accuracy when HOG and EfficientNet features were concatenation. The proposed concatenated model outperformed HOG and EfficientNet extracted features by providing higher accuracy and confusion matrix with the highest number of classified images. The study shows the advantages of the proposed model in terms of its accuracy in terms of identifying the vehicle make and model.
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CiteScore
1.40
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0.00%
发文量
45
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