Convolutional Neural Network Based Vehicle Classification in Adverse Illuminous Conditions for Intelligent Transportation Systems

Muhammad Atif Butt,Asad Masood Khattak,Ahthasham Sajid,Bashir Hayat,Sarmad Shafique,Ki-Il Kim,Awais Adnan,Muhammad Waqas Ayub,Saima Abid,M Irfan Uddin

doi:10.1155/2021/6644861

Abstract

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.

Highlights

With an exponential production of vehicles around the world, vehicle classification systems can play a significant role in the development of intelligent transportation systems, i.e., an automated highway toll collection, perception in self-driving vehicles, and traffic flow control systems
The dataset images are distributed into training, validation, and testing data, normalized to the size of 224 × 224 according to standard input size of Convolutional Neural Network (CNN) architectures. e training and testing images are randomly split by an 80–20% ratio of the total dataset images, and the validation set is formed by a random selection of 20% images from the training set
To evaluate the CNNs, AlexNet, Inception-v3, GoogleNet, VGG, and ResNet are loaded from Pytorch resources. e training of these networks is performed using the Pytorch framework; a stochastic gradient descent (SGD) optimizer is employed for the parameter learning with momentum, learning rate, and batch size of 0.9, 0.001, and 128, respectively

Summary

Introduction

With an exponential production of vehicles around the world, vehicle classification systems can play a significant role in the development of intelligent transportation systems, i.e., an automated highway toll collection, perception in self-driving vehicles, and traffic flow control systems. Laser and loop induction sensors-based methods have been proposed for the vehicle type classification [1,2,3,4]. Ese sensors have been installed under the pavement of the roads to collect and analyse the data to extract the relevant information regarding vehicles. In step with the advancement in computer vision, image processing and pattern recognition-based vehicle classification systems have been proposed [6, 7]. Computer vision-based classification system is a two-step procedure; in the first step, handcrafted extraction methods are utilized to obtain visual features from input visual frame. Machine learning classifiers are trained on the extracted features to perform classification on group-based data. Hand-crafted features are categorized into (i) global and (ii) local features to describe and represent the image data simultaneously [8]

Methods

Results

Discussion

Conclusion