Quantity Detection of Steel Bars Based on Deep Learning - Open Access Library Journal

OALibJ > Vol.6 No.10, October 2019

Quantity Detection of Steel Bars Based on Deep Learning ()

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DOI: 10.4236/oalib.1105784 566 Downloads 2,440 Views Citations

Author(s)

Han Yang, Chenghua Fu

Affiliation(s)

College of Mechanical Engineering of Sichuan University of Science & Engineering, Zigong, China.

ABSTRACT

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.

KEYWORDS

Deep Learning, Object Detection, Steel Bar Detection

Share and Cite:

Yang, H. and Fu, C. (2019) Quantity Detection of Steel Bars Based on Deep Learning. Open Access Library Journal, 6, 1-9. doi: 10.4236/oalib.1105784.

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