Abstract
In order to improve the real-time efficiency of expressway operation monitoring and management, the anomaly detection in intelligent monitoring network (IMN) of expressway based on edge computing and deep learning is studied. The video data collected by the camera equipment in the IMN structure of the expressway is transmitted to the edge processing server for screening and then sent to the convolution neural network. Then video data was preprocessed after the edge calculation to generate the training sample set, then send it to the AlexNet model for feature extraction. SVM classifier model is used to train the feature data set and input the features of the test samples into the trained SVM classifier model to realize the anomaly detection in the IMN of expressway. The experimental results showed that the method has better detection effect than the machine learning method and the small block learning method, and the detection time is greatly shortened.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Li, Y., Wang, Z.W., Xu, S.F.: Image encryption algorithm based on multiple-parameter fractional fourier and Arnold transforms. J. China Acad. Electron. Inf. Technol. 11(2), 164–168 (2016)
Zhou, Y., Nejati, H., Do, T.T., et al.: Image-based vehicle analysis using deep neural network: A systematic study. In: 2016 IEEE International Conference on Digital Signal Processing (DSP), Beijing, China, pp. 276–280. IEEE (2016)
Mansour, A., Hassan, A., Hussein, W.M.: Automated vehicle detection in satellite images using deep learning. IOP Conf. Ser. Mater. Sci. Eng. 610(1), 012–027 (2019)
Jiang, N., Tian, F., Li, J., et al.: MAN: mutual attention neural networks model for aspect-level sentiment classification in SIoT. IEEE Internet Things J. 7(4), 2901–2913 (2020)
Jiang, N., Chen, J., Zhou, R.G., et al.: PAN: pipeline assisted neural networks model for data-to-text generation in social internet of things. Inf. Sci. 530, 167–179 (2020)
Zhang, R., Wang, J., Chen, Z.X.: wireless digital communication technology for parallel DC-DC converter. J. Power Supply 16(3), 44–47 (2018)
Yuan, G., Zhong, F.Q.: On computing the edge-connectivity of an uncertain graph. IEEE Trans. Fuzzy Syst. 24(4), 981–991 (2016)
Daniel, H., Jun, S., Taku, K.: A deep learning framework for character motion synthesis and editing. ACM Trans. Graph. 35(4), 1–11 (2016)
Eric, L.F., Rishi, R., Stefan, B.W.: Deep learning approach to passive monitoring of the underwater acoustic environment. J. Acoust. Soc. Am. 140(4), 3351 (2016)
Ruo, M.Y., Ling, S.: Blind image blur estimation via deep learning. IEEE Trans. Image Process. 25(4), 1910–1921 (2016)
Nguyen, T., Bui, V., Lam, V.: Automatic phase aberration compensation for digital holographic microscopy based on deep learning background detection. Opt. Express 25(13), 15043–15057 (2017)
Ji, Q.G., Lu, Z.M., Chi, R.: Real-time multi-feature based fire flame detection in video. Image Process. IET 11(1), 31–37 (2016)
Xu, Yu., Li, D., Wang, Z., Guo, Q., Xiang, W.: A deep learning method based on convolutional neural network for automatic modulation classification of wireless signals. Wireless Netw. 25(7), 3735–3746 (2018). https://doi.org/10.1007/s11276-018-1667-6
Jin, Y.F., Gao, Y.: Optimal piecewise real-time pricing strategy for smart grid. Comput Simul. 33(4), 171–175 (2016)
Jiang, N., Xu, D., Zhou, J., et al.: Toward optimal participant decisions with voting-based incentive model for crowd sensing. Inf. Sci. 512, 1–17 (2020)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Wang, J., Wang, M., Yin, G., Zhang, Y. (2020). DAD: Deep Anomaly Detection for Intelligent Monitoring of Expressway Network. In: Chen, X., Yan, H., Yan, Q., Zhang, X. (eds) Machine Learning for Cyber Security. ML4CS 2020. Lecture Notes in Computer Science(), vol 12486. Springer, Cham. https://doi.org/10.1007/978-3-030-62223-7_6
Download citation
DOI: https://doi.org/10.1007/978-3-030-62223-7_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-62222-0
Online ISBN: 978-3-030-62223-7
eBook Packages: Computer ScienceComputer Science (R0)