Affiliations: [a] School of Transportation, Southeast University, Nanjing, P.R. China
| [b] School of Computer and Data Engineering, NingboTech University, Ningbo, P.R. China
| [c] Shandong Hi-speed Group Co., Ltd, Jinan, P.R. China
Correspondence:
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Corresponding author. Chihang Zhao, School of Transportation, Southeast University, Nanjing, P.R. China. E-mail: [email protected].
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.
Keywords: Vehicle types, convolutional neural networks, fused deep convolutional neural networks, intelligent transportation systems
