A billet is dispatched from the inventory to the bench by a crane and then from the bench to the front of the furnace through a track. In the past, the billet was pushed onto the track by the manual control of machinery. The automation of this process requires knowledge of the real-time position distribution of billets on the bench for automatic control of the pusher. In this study, the real-time positioning of billets on the bench is achieved by the machine vision method. Specifically, with the U-Net as the basic network, the residual blocks in classic ResNet are used to achieve the accurate segmentation of transverse positions of billets. The experimental results and field application tests indicate that the segmentation accuracy of this method can meet the control requirements of industrial fields.
[2] Bouthemy P, Francois E. Motion segmentation and qualitative dynamic scene analysis from an image sequence. International Journal of Computer Vision, 1993, 10(2): 157–182. [doi: 10.1007/BF01420735
[3] Lowe DG. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 2004, 60(2): 91–110. [doi: 10.1023/B:VISI.0000029664.99615.94
[5] Everingham M, Eslami SMA, van Gool L, et al. The PASCAL visual object classes challenge: A retrospective. International Journal of Computer Vision, 2015, 111(1): 98–136. [doi: 10.1007/s11263-014-0733-5
[6] Uijlings JRR, van de Sande KEA, Gevers T, et al. Selective search for object recognition. International Journal of Computer Vision, 2013, 104(2): 154–171. [doi: 10.1007/s11263-013-0620-5
[7] Gould S, He XM. Scene understanding by labeling pixels. Communications of the ACM, 2014, 57(11): 68–77. [doi: 10.1145/2629637
[8] Otsu N. A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics, 1979, 9(1): 62–66. [doi: 10.1109/TSMC.1979.4310076
[10] Lakshmi S, Sankaranarayanan DV. A study of edge detection techniques for segmentation computing approaches. International Journal of Computer Applications, 2010, CASCT(1): 35–41. [doi: 10.5120/993-25
[11] Adams R, Bischof L. Seeded region growing. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1994, 16(6): 641–647. [doi: 10.1109/34.295913
[12] Hinton GE, Osindero S, Teh YW. A fast learning algorithm for deep belief nets. Neural Computation, 2006, 18(7): 1527–1554. [doi: 10.1162/neco.2006.18.7.1527
[13] Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. Proceedings of the 25th International Conference on Neural Information Processing Systems. Lake Tahoe: Curran Associates Inc., 2012. 1097–1105.
[14] Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. Proceedings of the 3rd International Conference on Learning Representations. San Diego: ICLR, 2014. 1409–1556.
[15] He KM, Zhang XY, Ren SQ, et al. Deep residual learning for image recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas: IEEE, 2016. 770–778.
[16] Ronneberger O, Fischer P, Brox T. U-Net: Convolutional networks for biomedical image segmentation. Proceedings of 18th International Conference on Medical Image Computing and Computer-Assisted Intervention. Munich: Springer, 2015. 234–241.
[17] Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation. Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition. Boston: IEEE, 2015. 3431–3440.