Abstract
For the problems that the traditional mechanical parts identification algorithm needs to design and extract relevant features artificially, so that the process is complex and time consuming in the computation is larger as well as identification accuracy is easily affected by the diversity of parts morphology, a mechanical part identification algorithm based on convolutional neural network is proposed in this paper. The Leaky ReLU function algorithm as an activation function is used to improve the pooling method, and a SVM classifier is combined to construct a convolutional neural network WorkNet-2 for the recognition of mechanical parts. In the recognition experiments of common four kinds of mechanical parts, the trained WorkNet-2 network’s recognition accuracy on the test set reached 97.82%. The experimental results show that compared with the traditional mechanical parts recognition algorithm, this algorithm can extract the high-level features of the target parts, and has the advantages of small influence of parts shape diversity, the recognition rate is higher and good real-time performance.
Keywords
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Zhao, P.: Machine Vision Theory and Application, pp. 28–29. Electronic Industry Press (2011)
Yang, D., Zhang, Z.: Research on the method of workpiece location and recognition using image segmentation. Small Microcomput. Syst. 37(9), 2070–2073 (2016)
Liu, P., Shen, Y., Yao, F., et al.: Region-based moving object detection using HU moments. In: IEEE International Conference on Information and Automation, pp. 1590–1593. IEEE (2015)
Cao, J., Yuan, A., Yu, L.: A part recognition algorithm based on SURF feature. Comput. Appl. Softw. 32(1), 186–189 (2015)
He, X., Xie, Q., Xu, H.: Part recognition system based on LabVIEW and BP neural network. Instrum. Technol. Sens. (1), 119–122 (2017)
Chang, L., Deng, X., Zhou, M., et al.: Convolution neural network in image understanding. Autom. J. 42(9), 1300–1312 (2016)
Dong, J., Jin, L., Zhou, F.: Summary of convolution neural network research. Acta Comput. Sci. 40(6), 1229–1251 (2017)
Li, T., Li, X., Ye, M.: Summary of research on target detection based on convolution neural network. Comput. Appl. Res. 34(10), 2881–2886 (2017)
Deng, L., Wang, Z.J.: Vehicle recognition based on deep convolution neural network. Comput. Appl. Res. 33(3), 930–932 (2016)
Gold, J., Liu, W., Wang, X.: Traffic sign recognition based on optimized convolution neural network. Comput. Appl. 37(2), 530–534 (2017)
Hua, K.L., Hsiao, Y.S., Sanchez-Riera, J., et al.: A comparative study of data fusion for RGB-D based visual recognition. Pattern Recognit. Lett. 73(10), 1–6 (2016)
Huang, G., Sun, L., Wu, X.: Fast vision recognition and localization algorithm for industrial sorting robot based on deep learning. Robot 38(6), 711–719 (2016)
Chen, T., Wang, N., Xu, B., et al.: Empirical evaluation of rectified activations in convolutional network. Comput. Sci. 209–213 (2015)
He, K., Ren, S., Zhang, X., et al.: Delving deep into rectifiers: surpassing human-level performance on ImageNet classification. In: IEEE International Conference on Computer Vision, pp. 1026–1034 (2015)
He, K., Ren, S., Zhang, X., et al.: Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans. Pattern Anal. Mach. Intell. 37(9), 1904–1916 (2015)
Jiang, S.: Image Recognition based on Convolution Neural Network, pp. 14–17. Jilin University (2017)
Dong, L., Li, P., Xiao, H., et al.: A cloud image detection method based on SVM vector machine. Neurocomputing 169, 34–42 (2015)
Acknowledgments
This study was funded by the project of Jiangsu science and technology plan (BEK2013671); grants from the Jiangsu Province University Academic Degree Graduates Scientific Research and Innovation Plan (KYLX16_0630).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Suolin, D., Congcong, Y., Maomao, L. (2019). Recognition Algorithm Based on Convolution Neural Network for the Mechanical Parts. In: Wang, K., Wang, Y., Strandhagen, J., Yu, T. (eds) Advanced Manufacturing and Automation VIII. IWAMA 2018. Lecture Notes in Electrical Engineering, vol 484. Springer, Singapore. https://doi.org/10.1007/978-981-13-2375-1_42
Download citation
DOI: https://doi.org/10.1007/978-981-13-2375-1_42
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-2374-4
Online ISBN: 978-981-13-2375-1
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)