Abstract
Automatic workpiece grabbing on production line is important for improving the production efficiency in manufacturing industry. However, due to their irregular shapes, uncertain positions, and various posture changes, traditional edge detection, feature extraction and other methods are difficult to accurately identify and locate complex workpieces. In this paper, we propose an approach to recognize workpieces and determine their postures with deep learning. Based on object detection, input images containing workpieces are fed into an angle regression network which is used to determine the three-dimensional posture of workpieces. The classification, position and posture information are obtained from the final output. Experiments show that this method is more robust and can achieve higher accuracy than traditional feature extraction methods.
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References
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. 1, pp. 886–893. IEEE (2005)
Felzenszwalb, P., Girshick, R., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part-based models. IEEE Trans. Pattern Anal. Mach. Intell. 32(9), 1627–1645 (2010)
Rosenblatt, F.: Perceptron simulation experiments. J. Proc. IRE 48(3), 301–309 (1960)
Dahl, G.E., Yu, D., Deng, L., Acero, A.: Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition. IEEE Trans. Audio Speech Lang. Process. 20(1), 30–42 (2012)
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)
LeCun, Y., Bengio, Y.: Convolutional networks for images, speech, and time series. In: The Handbook of Brain Theory and Neural Networks, vol. 3361, no. 10 (1995)
Hinton, G.E., Osindero, S., Teh, Y.W.: A fast learning algorithm for deep belief nets. J. Neural Comput. 18(7), 1527–1554 (2006)
Ranzato, M., Boureau, Y.L., Chopra, S., Lecun, Y.: A unified energy-based framework for unsupervised learning. In: Proceedings of Conference on AI and Statistics, pp. 860–867 (2007)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. International Conference on Neural Information Processing Systems, vol. 60, pp. 1097–1105. Curran Associates Inc. (2012)
Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014)
He, K., Zhang, X., Ren, S., Sun, J.: Spatial pyramid pooling in deep convolutional networks for visual recognition. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8691, pp. 346–361. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10578-9_23
Girshick, R.: Fast R-CNN. Computer Science (2015)
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, pp. 91–99 (2015)
Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016)
Beattie, C., Leibo, J.Z., Teplyashin, D.: DeepMind Lab (2016)
Yuan-Dong, T.: A simple analysis of AlphaGo. J. Acta Autom. Sin. 42, 671–675 (2016)
Yu, X., Gao, G., He, W., Xu, J.: Machine vision based automatic micro-parts detection system. In: Sun, C., Fang, F., Zhou, Z.-H., Yang, W., Liu, Z.-Y. (eds.) IScIDE 2013. LNCS, vol. 8261, pp. 160–167. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-42057-3_21
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This work is supported by China Postdoctoral Science Foundation (2017M612047) and Qianjiang Talent Program (QJD1702031).
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Xu, X., Peng, C., Xiao, J., Jing, H., Wu, X. (2018). A Fast Positioning Algorithm Based on 3D Posture Recognition. In: Peng, Y., Yu, K., Lu, J., Jiang, X. (eds) Intelligence Science and Big Data Engineering. IScIDE 2018. Lecture Notes in Computer Science(), vol 11266. Springer, Cham. https://doi.org/10.1007/978-3-030-02698-1_31
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