Abstract
Aim: This study proposed an application of convolutional neural network (CNN) on vehicle identification of Ford motorcar. We used single camera to obtain vehicle images from side view. Method: We collected a 100-image dataset, among which 50 were Ford motorcars and 50 were non-Ford motorcars. We used data augmentation to enlarge its size to 3900-image. Then, we developed an eight-layer CNN, which was trained by stochastic gradient descent with momentum method. Results: Our CNN method achieves a sensitivity of 93.64%, a specificity of 93.13, and an accuracy of 93.38%. Conclusion: This proposed CNN method performs better than three state-of-the-art approaches.
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Acknowledgements
This paper is supported by Natural Science Foundation of China (61602250), Natural Science Foundation of Jiangsu Province (BK20150983), Open fund of Key Laboratory of Guangxi High Schools Complex System and Computational Intelligence (2016CSCI01), Open Fund of Fujian Provincial Key Laboratory of Data Intensive Computing (BD201607).
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Wang, SH., Jia, WJ., Zhang, YD. (2017). Ford Motorcar Identification from Single-Camera Side-View Image Based on Convolutional Neural Network. In: Yin, H., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2017. IDEAL 2017. Lecture Notes in Computer Science(), vol 10585. Springer, Cham. https://doi.org/10.1007/978-3-319-68935-7_20
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DOI: https://doi.org/10.1007/978-3-319-68935-7_20
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