Steel Bar Counting from Images with Machine Learning

Counting has become a fundamental task for data processing in areas such as microbiology, medicine, agriculture and astrophysics. The proposed SA-CNN-DC (Scale Adaptive-Convolutional Neural Network-Distance Clustering) methodology in this paper is designed for automated counting of steel bars from images. Its design consists of two Machine Learning techniques: Neural Networks and Clustering. The system has been trained to count round and squared steel bars, obtaining an average detection accuracy of 98.81% and 98.57%, respectively. In the steel industry, counting steel bars is a time consuming task which highly relies on human labour and is prone to errors. Reduction of counting time and resources, safety and productivity of employees and high confidence of the inventory are some of the advantages of the proposed methodology in a steel warehouse.

Automated summary

The SA-CNN-DC methodology proposed in this paper utilizes neural networks and clustering techniques for automated counting of steel bars, improving accuracy and efficiency in counting. The method offers various advantages in a steel warehouse, such as reducing counting time and resources, ensuring employee safety and productivity, and increasing confidence in inventory management.