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一种基于FDLSR的新技术,用于视图独立的车辆品牌和型号识别

Sobia Hayee¹, Fawad Hussain¹, Muhammad Haroon Yousaf^1,2

¹Department of Computer Engineering, University of Engineering & Technology, Taxila 47050, Pakistan.

Sensors (Basel, Switzerland)

|September 28, 2023

概括

本研究提出了一种新的混合CNN模型,用于准确的车辆制造和型号识别 (VMMR),尽管具有挑战性的图像条件和复杂的分类. 该模型有效地提取细粒度的特征,以获得强大的VMMR性能.

科学领域:

计算机视觉计算机视觉
人工智能的人工智能
智能运输系统智能运输系统

背景情况:

车辆品牌和型号识别 (VMMR) 对智能运输系统 (ITS) 至关重要.
影子,天气和遮蔽等图像挑战使VMMR复杂化.
多类分类问题,包括多重性和模糊性,阻碍了准确的VMMR.

研究的目的:

推出一种新且强大的VMMR模型.
解决VMMR中的挑战,包括图像变异和分类复杂性.
为了达到与最先进的方法相提并论的准确性.

主要方法:

开发了一个混合卷积神经网络 (CNN) 模型.
用费舍尔歧视最小平方回归 (FDLSR) 来进行特征选择.
深度CNN模型在Stanford-196和BoxCars21k数据集上使用ResNet-152功能进行了微调.

主要成果:

拟议的混合CNN模型在VMMR中表现出高精度.
与SVM和FC层相比,Stanford-196的性能提高了0.5%和4%.与SVM和FC层相比,Stanford-196的性能提高了0.5%和4%.
在BoxCars21k上实现了0.4%和1%的精度增长,超过SVM和FC层.

结论:

新的混合CNN模型为VMMR提供了一个强大的解决方案.
该模型有效地处理图像挑战和多类分类问题.
这种方法非常适合小型,细粒度的车辆数据集.

关键词:

费舍尔的歧视最小平方回归.这是一个VMMR.模两可的模两可的混合CNN模型混合CNN模型多类分类是多类分类的分类.多重性的多重性多重性.小规模的细粒度车辆数据集.

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