99787 20230519145409.0 doi 10.3390/electronics10040402 sideral 123327 ART-2021-123327 eng Hernández-Ruiz, A.C. Steel bar counting from images with machine learning 2021 Access copy available to the general public Unrestricted 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. info:eu-repo/semantics/openAccess by http://creativecommons.org/licenses/by/3.0/es/ 2.69 2021 COMPUTER SCIENCE, INFORMATION SYSTEMS 100 / 164 = 0.61 2021 Q3 T2 PHYSICS, APPLIED 82 / 161 = 0.509 2021 Q3 T2 ENGINEERING, ELECTRICAL & ELECTRONIC 139 / 277 = 0.502 2021 Q3 T2 0.59 2021 Computer Networks and Communications 2021 Q2 Signal Processing 2021 Q2 Hardware and Architecture 2021 Q2 Control and Systems Engineering 2021 Q2 3.7 2021 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Martínez-Nieto, J.A. Universidad de Zaragoza (orcid)0000-0002-1405-1808 Buldain-Pérez, J.D. Universidad de Zaragoza (orcid)0000-0003-3431-5863 5008 785 Universidad de Zaragoza Dpto. Ingeniería Electrón.Com. Área Tecnología Electrónica 5008 250 Universidad de Zaragoza Dpto. Ingeniería Electrón.Com. Área Electrónica 10, 4 (2021), 402 [19 pp] Electronics (Basel) Electronics 2079-9292 811902 http://zaguan.unizar.es/record/99787/files/texto_completo.pdf Versión publicada 2710247 http://zaguan.unizar.es/record/99787/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:99787 articulos driver 2023-05-18-13:53:09 ARTICLE