Steel bar counting from images with machine learning

Hernández-Ruiz, A.C. ; Martínez-Nieto, J.A. (Universidad de Zaragoza) ; Buldain-Pérez, J.D. (Universidad de Zaragoza)

Resumen: Counting has become a fundamental task for data processing in areas such as micro-biology, 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.

Idioma: Inglés

DOI: 10.3390/electronics10040402

Año: 2021

Publicado en: Electronics 10, 4 (2021), 402 [19 pp]
ISSN: 2079-9292

Factor impacto JCR: 2.69 (2021)

Categ. JCR: COMPUTER SCIENCE, INFORMATION SYSTEMS rank: 100 / 164 = 0.61 (2021) - Q3 - T2
Categ. JCR: PHYSICS, APPLIED rank: 82 / 161 = 0.509 (2021) - Q3 - T2
Categ. JCR: ENGINEERING, ELECTRICAL & ELECTRONIC rank: 139 / 277 = 0.502 (2021) - Q3 - T2

Factor impacto CITESCORE: 3.7 - Computer Science (Q2) - Engineering (Q2)

Factor impacto SCIMAGO: 0.59 - Computer Networks and Communications (Q2) - Signal Processing (Q2) - Hardware and Architecture (Q2) - Control and Systems Engineering (Q2)

Tipo y forma: Artículo (Versión definitiva)

Área (Departamento): Área Tecnología Electrónica (Dpto. Ingeniería Electrón.Com.)
Área (Departamento): Área Electrónica (Dpto. Ingeniería Electrón.Com.)