Object Detection to Identify Shapes of Swallow Nests Using a Deep Learning Algorithm
Object detection is basic research in the field of computer vision to detect objects in an image or video. the TensorFlow framework is a widely adopted framework to create object detection programs and models. In this study, an object detection program and model are designed to detect the shape of a...
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| Format: | UMS Journal (OJS) |
| Language: | eng |
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Department of Informatics, Universitas Muhammadiyah Surakarta, Indonesia
2022
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| Online Access: | https://journals.ums.ac.id/index.php/khif/article/view/16489 |
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| author | Indrajaya, Denny Setiawan, Adi Hartanto, Djoko Hariyanto, Hariyanto |
| author_facet | Indrajaya, Denny Setiawan, Adi Hartanto, Djoko Hariyanto, Hariyanto |
| author_sort | Indrajaya, Denny |
| collection | OJS |
| description | Object detection is basic research in the field of computer vision to detect objects in an image or video. the TensorFlow framework is a widely adopted framework to create object detection programs and models. In this study, an object detection program and model are designed to detect the shape of a swallow's nest which consists of three classes, namely oval, angular, and bowl. The purpose model creation is to find out the likeliness of the swallow's nest to the three classes for the swallow's nest sorting machine. The adopted architecture in the modeling is the MobileNet V2 FPNLite SSD since the model obtained from this architecture results in a good speed in detecting objects. Based on the evaluation results that has been carried out, the model can detect the shape of the swallow's nest which is divided into 3 classes, but in some cases swallow's nest are detected into two classes. This issues can still be handled by adjustmenting several parameterss to the object detection program. Results shows that the obtained mAP value of 61.91%, indicating the model can detect the shape of a swallow's nest moderately. |
| format | UMS Journal (OJS) |
| id | oai:ojs2.journals.ums.ac.id:article-16489 |
| institution | Universitas Muhammadiyah Surakarta |
| language | eng |
| publishDate | 2022 |
| publisher | Department of Informatics, Universitas Muhammadiyah Surakarta, Indonesia |
| record_format | ojs |
| spelling | oai:ojs2.journals.ums.ac.id:article-16489 Object Detection to Identify Shapes of Swallow Nests Using a Deep Learning Algorithm Indrajaya, Denny Setiawan, Adi Hartanto, Djoko Hariyanto, Hariyanto object detection; swallow's nest; SSD MobileNet V2 FPNLite; classification; deep learning Object detection is basic research in the field of computer vision to detect objects in an image or video. the TensorFlow framework is a widely adopted framework to create object detection programs and models. In this study, an object detection program and model are designed to detect the shape of a swallow's nest which consists of three classes, namely oval, angular, and bowl. The purpose model creation is to find out the likeliness of the swallow's nest to the three classes for the swallow's nest sorting machine. The adopted architecture in the modeling is the MobileNet V2 FPNLite SSD since the model obtained from this architecture results in a good speed in detecting objects. Based on the evaluation results that has been carried out, the model can detect the shape of the swallow's nest which is divided into 3 classes, but in some cases swallow's nest are detected into two classes. This issues can still be handled by adjustmenting several parameterss to the object detection program. Results shows that the obtained mAP value of 61.91%, indicating the model can detect the shape of a swallow's nest moderately. Department of Informatics, Universitas Muhammadiyah Surakarta, Indonesia PT Waleta Asia Jaya Departemen Matematika dan Sains Data, Fakultas Sains dan Matematika, Universitas Kristen Satya Wacana 2022-10-13 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://journals.ums.ac.id/index.php/khif/article/view/16489 10.23917/khif.v8i2.16489 Khazanah Informatika : Jurnal Ilmu Komputer dan Informatika; Vol. 8 No. 2 October 2022 Khazanah Informatika; Vol. 8 No. 2 October 2022 2477-698X 2621-038X eng https://journals.ums.ac.id/index.php/khif/article/view/16489/7948 Copyright (c) 2022 Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika http://creativecommons.org/licenses/by/4.0 |
| spellingShingle | object detection; swallow's nest; SSD MobileNet V2 FPNLite; classification; deep learning Indrajaya, Denny Setiawan, Adi Hartanto, Djoko Hariyanto, Hariyanto Object Detection to Identify Shapes of Swallow Nests Using a Deep Learning Algorithm |
| title | Object Detection to Identify Shapes of Swallow Nests Using a Deep Learning Algorithm |
| title_full | Object Detection to Identify Shapes of Swallow Nests Using a Deep Learning Algorithm |
| title_fullStr | Object Detection to Identify Shapes of Swallow Nests Using a Deep Learning Algorithm |
| title_full_unstemmed | Object Detection to Identify Shapes of Swallow Nests Using a Deep Learning Algorithm |
| title_short | Object Detection to Identify Shapes of Swallow Nests Using a Deep Learning Algorithm |
| title_sort | object detection to identify shapes of swallow nests using a deep learning algorithm |
| topic | object detection; swallow's nest; SSD MobileNet V2 FPNLite; classification; deep learning |
| topic_facet | object detection; swallow's nest; SSD MobileNet V2 FPNLite; classification; deep learning |
| url | https://journals.ums.ac.id/index.php/khif/article/view/16489 |
| work_keys_str_mv | AT indrajayadenny objectdetectiontoidentifyshapesofswallownestsusingadeeplearningalgorithm AT setiawanadi objectdetectiontoidentifyshapesofswallownestsusingadeeplearningalgorithm AT hartantodjoko objectdetectiontoidentifyshapesofswallownestsusingadeeplearningalgorithm AT hariyantohariyanto objectdetectiontoidentifyshapesofswallownestsusingadeeplearningalgorithm |