Abstract
A scheme and algorithm for identifying and locating metal sodium bars and placing them into barrels intelligently were designed. In the detection and positioning of sodium bars, deep learning is mainly used to quickly identify sodium bars based on Open CV and YOLO convolutional neural network, and then morphological processing is carried out on each recognition frame of sodium bars to obtain the central location of sodium bars. When determining where the remaining sodium bars can be placed, it is assumed that the long axis of the sodium bar is tangent to the barrel wall and a cluster of circles is drawn to predict all possible positions of the sodium bars. Then, whether each circle has the intersection with other sodium bars is calculated one by one, so as to output the positions of the sodium bars that can be placed.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Pretto, A., Tonello, S., Menegatti, E.: Flexible 3d localization of planar objects for industrial bin-picking with mono camera vision system. In: 2013 IEEE International Conference on Automation Science and Engineering (CASE), IEEE (2013)
Yanai, K., Kawano, Y.: Food image recognition using deep convolutional network with pre-training and fine-tuning. IEEE International Conference on Multimedia and Expo Workshops, pp. 1–6. IEEE (2015)
Jianxiong, Z., Zhiguang, S., et al.: Automatic target recognition of SAR images based on global scattering center model. IEEE Trans. Geosci. Remote Sens. 49(10), 3713–3729 (2011)
Josien, P.W., Pluim, J.B., Maintz, A., Viergever, M.: Image registration by maximization of combined mutual information and gradient information. In: Delp, S.L., DiGoia, A.M., Jaramaz, B. (eds.) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2000, pp. 452–461. Springer Berlin Heidelberg, Berlin, Heidelberg (2000). https://doi.org/10.1007/978-3-540-40899-4_46
Cui, W., Wang, W., Liu, H.: Robust hand tracking with refined CAM Shift based on combination of depth and image features. In: 2012 IEEE International Conference on Robotics and Biomimetics (ROBIO), IEEE (2012)
Karpushin, M., Valenzise, G., Dufaux, F.: Local visual features extraction from texture+depth content based on depth image analysis. IEEE (2014)
Cheng, W.B., Li, C.P.: Design of image acquisition system based on machine vision. J. Guangxi. Teach. College. (Natural Science Edition) 23(002), 42–45 (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Pan, R., Li, G., Mitrouchev, P. (2022). Intelligent Recognition of Automatic Production Line of Metal Sodium Rod. In: Wang, Y., Martinsen, K., Yu, T., Wang, K. (eds) Advanced Manufacturing and Automation XI. IWAMA 2021. Lecture Notes in Electrical Engineering, vol 880. Springer, Singapore. https://doi.org/10.1007/978-981-19-0572-8_10
Download citation
DOI: https://doi.org/10.1007/978-981-19-0572-8_10
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-0571-1
Online ISBN: 978-981-19-0572-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)