A Web-Based Application for Identifying Objects in Images: Object Recognition Software

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dc.authorscopusid57211714315
dc.authorscopusid59160216000
dc.authorscopusid57188684428
dc.authorscopusid11738942300
dc.contributor.authorUcuzal H.
dc.contributor.authorBalikci Cicek A.G.I.
dc.contributor.authorArslan A.G.A.K.
dc.contributor.authorColak C.
dc.date.accessioned2024-08-04T20:04:00Z
dc.date.available2024-08-04T20:04:00Z
dc.date.issued2019
dc.departmentİnönü Üniversitesien_US
dc.description3rd International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2019 -- 11 October 2019 through 13 October 2019 -- 156063en_US
dc.description.abstractObject recognition is a computer vision technique for identifying objects in images or videos. Object recognition is an important output of deep learning and machine learning algorithms. For this purpose, open source, free and artificial intelligence based Object Recognition Software has been developed in order to perform object recognition operation easily.In creating this web-based software, Darkflow and Tensorflow libraries are used which are based on deep learning based Python programming language and allow the design of interactive web based applications. While performing object recognition analysis in the developed software, CNN (Convolutional Neural Networks) multiple convolution layers are uncovered hidden and useful features obtained by various calculation methods. With CNN, objects are classified, objects are detected, and objects are determned by image segmentation. A pre-trained model from COCO, a large-scale object detection, partitioning and image dataset, is used to see how the web-based software work and to evaluate the analysis outputs. Object recognition analysis is applied to ten images from this data set. According to the object recognition analysis results of the ten images, the calculated accuracy rates is examined and it is found that this web based software which is developed as open source and free access gives successful estimations in object recognition.In order to see how the web-based software works and to evaluate the analysis outputs, a pre-trained model was used from COCO (Common Objects in Context) which is a large scale object detection, partitioning and image dataset. Object recognition analysis was applied to ten images from this data set. When the accuracy ratio of the ten images calculated according to the object recognition analysis result is examined, it is determined that this web based software which is developed as open source and free access gives successful predictions in object recognition.The developed software is new user-friendly web-based software that can easily identify objects in images and discriminatory from each other objects. In the following studies, in order to increase the diagnostic accuracy of the objects in the images, it is suggested that the softwares that uses deeper neural networks should be developed and the necessary infrastructure to detect the defects in the medical images can be developed. © 2019 IEEE.en_US
dc.identifier.doi10.1109/ISMSIT.2019.8932735
dc.identifier.isbn9781728137896
dc.identifier.scopus2-s2.0-85078033266en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://doi.org/10.1109/ISMSIT.2019.8932735
dc.identifier.urihttps://hdl.handle.net/11616/92250
dc.indekslendigikaynakScopusen_US
dc.language.isotren_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartof3rd International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2019 - Proceedingsen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectdeep learningen_US
dc.subjectobject recognitionen_US
dc.subjectweb-based softwareen_US
dc.titleA Web-Based Application for Identifying Objects in Images: Object Recognition Softwareen_US
dc.title.alternativeGörüntülerdeki Nesneleri Tanimlamaya Yönelik Bir Web Tabanli Uygulama: Nesne Tanima Yazilimien_US
dc.typeConference Objecten_US

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