Steel Bar Counting from Images with Machine Learning

Abstract

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 the 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 that highly relies on human labor and is prone to errors. Reduction of counting time and resources, safety and productivity of employees, and high confidence in the inventory are some of the advantages of the proposed methodology in a steel warehouse.

Tasks

Reproductions