In the actual production environment, the number of steel bars in the con-struction site is mainly counted manually. For the special task of steel bar detection, a detection and counting method based on depth learning is proposed. The method is applied to the actual production environment in-stead of the traditional time-consuming and labor-consuming manual counting method. By comparing the traditional detection algorithm with the one-stage and two-stage detection in depth learning. After the algorithm and considering the efficiency of the model, the improved detection algorithm is proposed to adapt to the special task of steel bar detection. In the final evaluation index, the improved one-stage detection algorithm is superior to the improved detection algorithm in the special task of steel bar detection, showing the improvement of performance, and compared with the single-stage detection algorithm. The law has also been improved to a certain extent.
Cite this paper
Yang, H. and Fu, C. (2019). Quantity Detection of Steel Bars Based on Deep Learning. Open Access Library Journal, 6, e5784. doi: http://dx.doi.org/10.4236/oalib.1105784.
Felzenszwalb, P.F., Girshick, R.B., Ramanan, D. and McAllester, D. (2010) Object Detection with Discriminatively Trained Part-Based Models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32, 1627-1645. https://doi.org/10.1109/TPAMI.2009.167
Uijlings, J.R.R., Van De Sande, K.E.A., Smeulders, A.W.M. and Gevers, T. (2013) Selective Search for Object Recognition. International Journal of Computer Vision, 104, 54-171. https://doi.org/10.1007/s11263-013-0620-5
Hu, W. and Maybank, S. (2008) AdaBoost-Based Algorithm for Network Intrusion Detection. IEEE Transactions on Systems, Man, and Cybernetics, Part B, 38, 577-583. https://doi.org/10.1109/TSMCB.2007.914695
Krizhevsky, A., Sutskever, I. and Hinton, G.E. (2012) ImageNet Classification with Deep Convolutional Neural Networks. Advances in Neural Information Processing Systems, Harrahs and Harveys, Lake Tahoe, NV, 1097-1105.
Szegedy, C., Liu, W., Jia, Y., et al. (2015) Going Deeper with Convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, 7-12 June 2015, 1-9. https://doi.org/10.1109/CVPR.2015.7298594
Ren, S., He, K. and Girshick, R. (2015) Faster R-CNN: Towards Real-Time Object Detection with Regional Proposal Networks. Advances in Neural Information Processing Systems, Montreal, 7-12 December 2015, 91-99.
Redmon, J., Divvala, S. and Girshick, R. (2016) You Only Look Once: Unified, Real-Time Object Detection. 2016 Conference on Computer Vision and Pattern Recognition. Las Vegas, NV, 27-30 June 2016, 779-788. https://doi.org/10.1109/CVPR.2016.91
Liu, W., Anguelov, D., Erhan, D., et al. (2016) SSD: Single Shot MultiBox Detector. In: Leibe, B., Matas, J., Sebe, N. and Welling, M., Eds., Computer Vision-ECCV 2016. Lecture Notes in Computer Science, Springer, Cham, October 8-16 2016, 21-37. https://doi.org/10.1007/978-3-319-46448-0_2
Lin, T.Y., Goyal, P. and Girshick, R. (2017) Focal Loss for Dense Object Detection. 2017 IEEE International Conference on Computer Vision, Venice, Italy, 22-29 October 2017, 2980-2988. https://doi.org/10.1109/ICCV.2017.324
