  Automated steel bar counting and center localization plays an important role
in the factory automation of steel bars. Traditional methods only focus on
steel bar counting and their performances are often limited by complex
industrial environments. Convolutional neural network (CNN), which has great
capability to deal with complex tasks in challenging environments, is applied
in this work. A framework called CNN-DC is proposed to achieve automated steel
bar counting and center localization simultaneously. The proposed framework
CNN-DC first detects the candidate center points with a deep CNN. Then an
effective clustering algorithm named as Distance Clustering(DC) is proposed to
cluster the candidate center points and locate the true centers of steel bars.
The proposed CNN-DC can achieve 99.26% accuracy for steel bar counting and 4.1%
center offset for center localization on the established steel bar dataset,
which demonstrates that the proposed CNN-DC can perform well on automated steel
bar counting and center localization. Code is made publicly available at:
https://github.com/BenzhangQiu/Steel-bar-Detection.
