Paper Info
Title
Classification of vehicle types using fused deep convolutional neural networks
Abstract
Classification of vehicle types using surveillance images is a challenging task in Intelligent Transportation Systems (ITS). In this paper, Convolutional Neural Networks for Vehicle types classification are comparatively studied. Firstly, GoogLeNet, ResNet50 and InceptionV4 are exploited as baselines for comparison. Secondly, we proposed a new network architecture based on GoogLeNet, ResNet50 and InceptionV4, named Fused Deep Convolutional Neural Networks (FDCNN), to take advantage of the 'Inception' module on parameter optimization and 'Residual' module on avoiding gradient vanishing, and applied the model to vehicle types classification. Thirdly, we created a vehicle dataset under the conditions of complicated and varied weather and lighting conditions, and conducted comparative experiments using the SEU vehicle dataset. Experimental results show much better performance of the proposed FDCNN with RMSprop optimizer on recognizing vehicle types. Specifically, the average classification accuracies of six vehicle types, such as truck, coach, sedan, minivan, pickup and SUV, are over 96.8%. Among the six classes of vehicle types, sedan is the most difficult to classify and the proposed FDCNN achieved over 93.81% accuracy in comparative experiments.
Year
DOI
Venue
2022
10.3233/JIFS-211505
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
Keywords
DocType
Volume
Vehicle types, convolutional neural networks, fused deep convolutional neural networks, intelligent transportation systems
Journal
42
Issue
ISSN
Citations 
6
1064-1246
0
PageRank 
References 
Authors
0.34
0
9
Name
Order
Citations
PageRank
Zichen Qian100.34
Chihang Zhao2306.20
Bailing Zhang300.68
Shengmei Lin400.34
Liru Hua500.34
Hao Li600.34
Xiaogang Ma700.34
Teng Ma800.34
Xinliang Wang900.34