Pretraining Convolutional Neural Networks for Image-Based Vehicle Classification

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ID: 7765
2018
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Abstract
Vehicle detection and classification are very important for analysis of vehicle behavior in intelligent transportation system, urban computing, etc. In this paper, an approach based on convolutional neural networks (CNNs) has been applied for vehicle classification. In order to achieve a more accurate classification, we removed the unrelated background as much as possible based on a trained object detection model. In addition, an unsupervised pretraining approach has been introduced to better initialize CNNs parameters to enhance the classification performance. Through the data enhancement on manual labeled images, we got 2000 labeled images in each category of motorcycle, transporter, passenger, and others, with 1400 samples for training and 600 samples for testing. Then, we got 17395 unlabeled images for layer-wise unsupervised pretraining convolutional layers. A remarkable accuracy of 93.50% is obtained, demonstrating the high classification potential of our approach.
Reference Key
yunfei2018pretrainingadvances Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Han, Yunfei;Jiang, Tonghai;Ma, Yupeng;Xu, Chunxiang;Han, Yunfei;Jiang, Tonghai;Ma, Yupeng;Xu, Chunxiang;
Journal advances in multimedia
Year 2018
DOI
10.1155/2018/3138278
URL
https://www.hindawi.com/journals/am/2018/3138278/abs/
Keywords Keywords not found

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