Research on intelligent real-time identification system of traffic signs based on improved recurrent neural network algorithm

Authors

WANG Shaoying, Huanghe Jiaotong University, Jiaozuo, Henan 454950, ChinaFollow

Abstract

Objective： In order to meet the needs of the edge computing of the Internet of Things, a recurrent neural network algorithm was introduced for the first time in this paper, and an intelligent real-time classification and recognition system was constructed to conduct research on food packaging images. Methods： To build a simulation experiment test model, firstly preprocessed the image data set, de-redundantize, grayscale, and normalized the two-dimensional image, and finally input the time-sequential data in parallel. Using a typical memristor as the research object of realizing hardware RNN, the nonlinear function of memristor was used to construct the mapping layer of parallel array reserve pool neural network. the ridge regression algorithm was used to solve the problems of overfitting in the training process. Results: The classification accuracy of the food packaging data set was as high as 98.59%. Conclusion： The system reduces the number of traditional neural network layers, reduces the training cost, and realizes high-precision real-time online recognition of time series signals.

Publication Date

10-30-2023

First Page

110

Last Page

116

DOI

10.13652/j.spjx.1003.5788.2023.60110

Recommended Citation

Shaoying, WANG (2023) "Research on intelligent real-time identification system of traffic signs based on improved recurrent neural network algorithm," Food and Machinery: Vol. 39: Iss. 9, Article 17.
DOI: 10.13652/j.spjx.1003.5788.2023.60110
Available at: https://www.ifoodmm.cn/journal/vol39/iss9/17

References

[1] SURESH P, DANIEL J V, PARTHASARATHY V, et al. A state of the art review on the Internet of Things （IoT） history, technology and fields of deployment[C]// 2014 International Conference on Science Engineering and Management Research （ICSEMR）. [S.l.]: IEEE, 2014: 1-8.
[2] AL-FUQAHA A, GUIZANI M, MOHAMMADI M, et al. Internet of things: A survey on enabling technologies, protocols, andapplications[J]. IEEE Communications Surveys & Tutorials, 2015, 17（4）: 2 347-2 376.
[3] SHI W, CAO J, ZHANG Q, et al. Edge computing: Vision andchallenges[J]. IEEE Internet of Things Journal, 2016, 3（5）: 637-646.
[4] XIN Y. Evolving artificial neuralnetworks[J]. Proceedings of the IEEE, 1999, 87（9）: 1 423-1 447.
[5] 吴昊然, 陈晓星, 高傲. 基于深度卷积神经网络的食品包装缺陷检测算法研究[J]. 智能计算机与应用, 2023, 13（3）: 10-15. WU H R, CHEN X X, GAO A. Research on food packaging defect detection algorithm based on deep convolutional neural network[J]. Intelligent Computer and Applications, 2023, 13（3）: 10-15.
[6] MEDUS L D, SABAN M, FRANCES-VILLORA J V, et al. Hyperspectral image classification using CNN: Application to industrial food packaging[J]. Food Control, 2021, 125: 107962.
[7] THOTA M, KOLLIAS S, SWAINSON M, et al. Multi-source domain adaptation for quality control in retail food packaging[J]. Computers in Industry, 2020, 123: 103293.
[8] RIBEIRO F D S, CALIVA F, SWAINSON M, et al. An adaptable deep learning system for optical character verification in retail food packaging[C]// 2018 IEEE Conference on Evolving and Adaptive Intelligent Systems （EAIS）. [S.l.]: IEEE, 2018: 266-272.
[9] ATIYA A F, PARLOS A G. New results on recurrent network training: Unifying the algorithms and acceleratingconvergence[J]. IEEE Transactions on Neural Networks, 2000, 11（3）: 697-709.
[10] LUKOEVICIUS M, JAEGER H. Reservoir computing approaches to recurrent neural networktraining[J]. Computer Science Review, 2009, 3（3）: 127-149.
[11] VERSTRAETEN D, SCHRAUWEN B, D'HAENE M, et al. An experimental unification of reservoir computingmethods[J]. Neural Networks, 2007, 20（3）: 391-403.
[12] SUN J, YANG W, ZHENG T, et al. Novelnondelay-based reservoir computing with a single micromechanical nonlinear resonator for high-efficiency information processing[J]. Microsystems & Nanoengineering, 2021, 7（1）: 83.
[13] DU C, CAI F, ZIDAN M A, et al. Reservoir computing using dynamicmemristors for temporal information processing[J]. Nature Communications, 2017, 8（1）: 1-10.
[14] DUPORT F, SCHNEIDER B, SMERIERI A, et al. All-optical reservoircomputing[J]. Optics Express, 2012, 20（20）: 22783.
[15] PAQUOT Y, DUPORT F, SMERIERI A, et al. Optoelectronic reservoircomputing[J]. Scientific Reports, 2012, 2: 287.
[17] YAO P, WU H, GAO B, et al. Fully hardware-implemented memristor convolutional neural network[J]. Nature, 2020, 577（7 792）: 641-646.
[18] DAN C, MEIER U, MASCI J, et al. A committee of neural networks for traffic sign classification[C]// International Joint Conference on Neural Networks （IJCNN）. California: IEEE, 2011: 1 918-1 921.
[19] ZAKLOUTA F, STANCIULESCU B. Real-time traffic-sign recognition using tree classifiers[J]. IEEE Transactions on Intelligent Transportation Systems, 2012, 13（4）: 1 507-1 514.
[20] BOI F, GAGLIARDINI L. A support vector machines network for traffic sign recognition[C]// International Joint Conference on Neural Networks （IJCNN）. CA: IEEE, 2011: 2 210-2 216.