Humberto J.Cabeza BarretoIlker KurtulanSuleyman InciMert NakıpCüneyt GüzelişBarreto, Humberto J CabezaKurtulan, IlkerGuzelis, CuneytInci, SuleymanNakip, Mert2025-10-062020978172819136210.1109/ASYU50717.2020.92598492-s2.0-85097959802https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097959802&doi=10.1109%2FASYU50717.2020.9259849&partnerID=40&md5=11679a0cbdfec8d3d69e9d4b3d49c88bhttps://gcris.yasar.edu.tr/handle/123456789/9150https://doi.org/10.1109/ASYU50717.2020.9259849In this paper we proposed a system that automatically interprets the data of the utility meters by analyzing the photo of an analogue meter. In addition it sends the meter data to the consumers and the providers. We based the system on Convolutional Neural Networks (CNN) where we compared the You Only Look Once (YOLO) and a LeNet as CNN models. We collected the data for the training of each CNN model from the demonstration set of the project. Our results show that the YOLO model is reliable and fast. The model has a 99% accuracy for the gas meter and 98% accuracy for the water meter. © 2020 Elsevier B.V. All rights reserved.Englishinfo:eu-repo/semantics/closedAccessAnalog Meters, Convolutional Neural Networks, Image Segmentation, Lenet, Machine Learning, Yolo, Convolutional Neural Networks, Intelligent Systems, Cnn Models, Learning Models, Deep LearningConvolutional neural networks, Intelligent systems, CNN models, Learning models, Deep learningYOLOAnalog MetersConvolutional Neural NetworksMachine LearningImage SegmentationLenetConference Object