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Convolutional Neural Network Based Vehicle Classification in Adverse Illuminous Conditions for Intelligent Transportation Systems
Complexity ( IF 2.3 ) Pub Date : 2021-02-13 , DOI: 10.1155/2021/6644861
Muhammad Atif Butt 1 , Asad Masood Khattak 2 , Sarmad Shafique 3 , Bashir Hayat 4 , Saima Abid 3 , Ki-Il Kim 5 , Muhammad Waqas Ayub 6, 7 , Ahthasham Sajid 8 , Awais Adnan 4
Affiliation  

In step with rapid advancements in computer vision, vehicle classification demonstrates a considerable potential to reshape intelligent transportation systems. In the last couple of decades, image processing and pattern recognition-based vehicle classification systems have been used to improve the effectiveness of automated highway toll collection and traffic monitoring systems. However, these methods are trained on limited handcrafted features extracted from small datasets, which do not cater the real-time road traffic conditions. Deep learning-based classification systems have been proposed to incorporate the above-mentioned issues in traditional methods. However, convolutional neural networks require piles of data including noise, weather, and illumination factors to ensure robustness in real-time applications. Moreover, there is no generalized dataset available to validate the efficacy of vehicle classification systems. To overcome these issues, we propose a convolutional neural network-based vehicle classification system to improve robustness of vehicle classification in real-time applications. We present a vehicle dataset comprising of 10,000 images categorized into six-common vehicle classes considering adverse illuminous conditions to achieve robustness in real-time vehicle classification systems. Initially, pretrained AlexNet, GoogleNet, Inception-v3, VGG, and ResNet are fine-tuned on self-constructed vehicle dataset to evaluate their performance in terms of accuracy and convergence. Based on better performance, ResNet architecture is further improved by adding a new classification block in the network. To ensure generalization, we fine-tuned the network on the public VeRi dataset containing 50,000 images, which have been categorized into six vehicle classes. Finally, a comparison study has been carried out between the proposed and existing vehicle classification methods to evaluate the effectiveness of the proposed vehicle classification system. Consequently, our proposed system achieved 99.68%, 99.65%, and 99.56% accuracy, precision, and F1-score on our self-constructed dataset.

中文翻译:

智能交通系统中逆光照条件下基于卷积神经网络的车辆分类

随着计算机视觉的快速发展,车辆分类显示出重塑智能交通系统的巨大潜力。在过去的几十年中,基于图像处理和模式识别的车辆分类系统已被用来提高高速公路收费和交通自动监控系统的效率。但是,这些方法是在从小型数据集中提取的有限手工特征上训练的,这些特征无法满足实时道路交通状况。已经提出了基于深度学习的分类系统,以将上述问题纳入传统方法中。但是,卷积神经网络需要大量数据,包括噪声,天气和照明因素,以确保实时应用中的鲁棒性。而且,没有可用的通用数据集来验证车辆分类系统的功效。为了克服这些问题,我们提出了一种基于卷积神经网络的车辆分类系统,以提高实时应用中车辆分类的鲁棒性。我们提出了一个车辆数据集,其中包含10,000个图像,这些图像被分类为六种常见的车辆类别,考虑了不利的照明条件,以在实时车辆分类系统中实现鲁棒性。最初,对经过预训练的AlexNet,GoogleNet,Inception-v3,VGG和ResNet进行微调,以对自行构建的车辆数据集进行评估,以评估其准确性和收敛性。基于更好的性能,通过在网络中添加新的分类模块来进一步改进ResNet架构。为了确保泛化,我们在包含50,000张图像的公共VeRi数据集上对网络进行了微调,这些图像已分为六类车辆。最后,在建议的和现有的车辆分类方法之间进行了比较研究,以评估建议的车辆分类系统的有效性。因此,我们提出的系统在我们的自建数据集上实现了99.68%,99.65%和99.56%的准确性,精度和F1得分。
更新日期:2021-02-15
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