000099787 001__ 99787 000099787 005__ 20230519145409.0 000099787 0247_ $$2doi$$a10.3390/electronics10040402 000099787 0248_ $$2sideral$$a123327 000099787 037__ $$aART-2021-123327 000099787 041__ $$aeng 000099787 100__ $$aHernández-Ruiz, A.C. 000099787 245__ $$aSteel bar counting from images with machine learning 000099787 260__ $$c2021 000099787 5060_ $$aAccess copy available to the general public$$fUnrestricted 000099787 5203_ $$aCounting 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. 000099787 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/ 000099787 590__ $$a2.69$$b2021 000099787 592__ $$a0.59$$b2021 000099787 594__ $$a3.7$$b2021 000099787 591__ $$aCOMPUTER SCIENCE, INFORMATION SYSTEMS$$b100 / 164 = 0.61$$c2021$$dQ3$$eT2 000099787 593__ $$aComputer Networks and Communications$$c2021$$dQ2 000099787 591__ $$aPHYSICS, APPLIED$$b82 / 161 = 0.509$$c2021$$dQ3$$eT2 000099787 593__ $$aSignal Processing$$c2021$$dQ2 000099787 591__ $$aENGINEERING, ELECTRICAL & ELECTRONIC$$b139 / 277 = 0.502$$c2021$$dQ3$$eT2 000099787 593__ $$aHardware and Architecture$$c2021$$dQ2 000099787 593__ $$aControl and Systems Engineering$$c2021$$dQ2 000099787 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion 000099787 700__ $$0(orcid)0000-0002-1405-1808$$aMartínez-Nieto, J.A.$$uUniversidad de Zaragoza 000099787 700__ $$0(orcid)0000-0003-3431-5863$$aBuldain-Pérez, J.D.$$uUniversidad de Zaragoza 000099787 7102_ $$15008$$2785$$aUniversidad de Zaragoza$$bDpto. Ingeniería Electrón.Com.$$cÁrea Tecnología Electrónica 000099787 7102_ $$15008$$2250$$aUniversidad de Zaragoza$$bDpto. Ingeniería Electrón.Com.$$cÁrea Electrónica 000099787 773__ $$g10, 4 (2021), 402 [19 pp]$$pElectronics (Basel)$$tElectronics$$x2079-9292 000099787 8564_ $$s811902$$uhttps://zaguan.unizar.es/record/99787/files/texto_completo.pdf$$yVersión publicada 000099787 8564_ $$s2710247$$uhttps://zaguan.unizar.es/record/99787/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada 000099787 909CO $$ooai:zaguan.unizar.es:99787$$particulos$$pdriver 000099787 951__ $$a2023-05-18-13:53:09 000099787 980__ $$aARTICLE