Abstract
To tackle the problems of the dependence on source image clarity, the underutilization of source image information and the dependence on human-designed features in existing seat belt detection methods, a seat belt detection method using convolutional neural network (CNN) is proposed. In this paper, an improved convolutional neural network (called the BN-AlexNet) which adds the Batch Normalization (BN) module to the traditional convolutional neural network AlexNet is built to further enhance the classification ability of the convolutional neural network and greatly reduce the training difficulty. Later the confidence of detection results is analyzed, and the 95% confidence interval is used to set the rejection area. The result shows that the method achieves 92.51% correct detection rate by rejecting 6.50% test samples. Compared with the traditional methods based on image processing, the proposed method has higher correct detection rate.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Toroyan, T.: Global status report on road safety. Inj. Prev. 15(4), 286 (2009)
Zhang, T., Luo, X., Zhu, X.: License plate location based on singular value feature. In: IEEE International Conference on Computer Science and Information Technology, pp. 283–287 (2010)
Su, J., Ma, Z.: Car license plate location based on the density and projection. In: International Conference on Computational Intelligence and Natural Computing (CINC), pp. 409–412 (2009)
Chen, B., Cao, W., Zhang, H.: An efficient algorithm on vehicle license plate location. In: IEEE International Conference on Automation and Logistics (ICAL), pp. 1386–1389 (2008)
Hou, D.: Study on Vehicle Window Detection Technology. Beijing Jiaotong University (2011)
Agaian, S., Almuntashri, A.: Noise-resilient edge detection algorithm for brain MRI images. In: 31st Annual International Conference of the IEEE EMBS, Minneapolis, pp. 3689–3692 (2009)
Srivastava, G., Verma, R., Mahrishi, R., Rajesh, S.: A novel wavelet edge detection algorithm for noisy images. In: International Conference on Ultra Modern Telecommunications & Workshops ICUMT 2009, pp. 1–8 (2009)
Xie, T., Ping-An, M., Dai, S., et al.: Application of an improved method for extracting safety belt image feature. Inf. Technol. (2015)
Guo, H., Lin, H., Zhang, S., et al.: Image-based seat belt detection. In: IEEE International Conference on Vehicular Electronics and Safety, pp. 161–164 (2011)
Krizhevsky, A., Sutskever, I., Hinton, G.: ImageNet classification with deep convolutional neural networks. In: International Conference on Neural Information Processing Systems, pp. 1097–1105. Curran Associates Inc. (2012)
Szegedy, C., Liu, W., Jia, Y., et al.: Going deeper with convolutions. pp. 1–9 (2014)
Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. Computer Science (2015)
Nair, V., Hinton, G.: Rectified linear units improve restricted Boltzmann machines. In: International Conference on Machine Learning, pp. 807–814 (2010)
Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. J. Mach. Learn. Res. 9, 249–256 (2010)
Bouvrie, J.: Notes on convolutional neural networks. Neural Nets (2006)
Efron, B.: The Jackknife, the bootstrap and other resampling plans. J. Am. Stat. Assoc. 78(384), 316–331 (1982)
Hall. P.: The bootstrap and edgeworth expansion. Math. Gaz. 88(421) (1992)
Simonyan, K., Zisserman, A.: Very Deep Convolutional Networks for Large-Scale Image Recognition. Computer Science (2014)
Acknowledgement
This work is supported by National Natural Science Foundation of China, NO. 61273225 and NO. 61572381.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Zhou, B., Chen, D., Wang, X. (2017). Seat Belt Detection Using Convolutional Neural Network BN-AlexNet. In: Huang, DS., Bevilacqua, V., Premaratne, P., Gupta, P. (eds) Intelligent Computing Theories and Application. ICIC 2017. Lecture Notes in Computer Science(), vol 10361. Springer, Cham. https://doi.org/10.1007/978-3-319-63309-1_36
Download citation
DOI: https://doi.org/10.1007/978-3-319-63309-1_36
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-63308-4
Online ISBN: 978-3-319-63309-1
eBook Packages: Computer ScienceComputer Science (R0)