@inproceedings{874d00c61b9d4556811492e5e9b64105,
title = "Ford motorcar identification from single-camera side-view image based on convolutional neural network",
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.",
keywords = "Convolutional neural network, Ford motorcar, Side-view, Single camera, Vehicle identification",
author = "Wang, {Shui Hua} and Jia, {Wen Juan} and Zhang, {Yu Dong}",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2017.; 18th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2017 ; Conference date: 30-10-2017 Through 01-11-2017",
year = "2017",
doi = "10.1007/978-3-319-68935-7_20",
language = "English",
isbn = "9783319689340",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "173--180",
editor = "Hujun Yin and Minling Zhang and Yimin Wen and Guoyong Cai and Tianlong Gu and Tallon-Ballesteros, {Antonio J.} and Junping Du and Yang Gao and Songcan Chen",
booktitle = "Intelligent Data Engineering and Automated Learning – IDEAL 2017 - 18th International Conference, Proceedings",
}