An End to End Solution for Object Classification Using Artificial Neural Networks and Ultrasonic-Based Sensors
With the development of technology, target and object recognition issues are becoming increasingly important day by day. In this article, studies on ultrasonic signal processing and object recognition techniques in the literature were examined. An integrated system design that includes ultrasonic si...
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11006063/ |
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| author | Ahmet Karagoz Gokhan Dindis |
| author_facet | Ahmet Karagoz Gokhan Dindis |
| author_sort | Ahmet Karagoz |
| collection | DOAJ |
| description | With the development of technology, target and object recognition issues are becoming increasingly important day by day. In this article, studies on ultrasonic signal processing and object recognition techniques in the literature were examined. An integrated system design that includes ultrasonic signal data obtained from different objects has been designed to contribute to the literature. With this system design, a new method is proposed that includes signal pre-processing, feature extraction and classification processes. In addition, different machine learning and artificial neural networks algorithms are used to organize and classify the data obtained from ultrasonic signals, to obtain high-performance classification rates and to achieve comprehensive object recognition. As a result of the study carried out in this context, the data set obtained from different types of objects at different distances and angles was arranged and passed through various pre-processing and statistical feature extraction methods. In conclusion, a high-performance classification rate of 97.8% using the random forest algorithm and 99.10% using the artificial neural networks algorithm was obtained during the classification phase. |
| format | Article |
| id | doaj-art-e40ca4e0d20b46988dc0f6aa5bb275a8 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-e40ca4e0d20b46988dc0f6aa5bb275a82025-08-20T02:26:50ZengIEEEIEEE Access2169-35362025-01-0113872548726710.1109/ACCESS.2025.357076211006063An End to End Solution for Object Classification Using Artificial Neural Networks and Ultrasonic-Based SensorsAhmet Karagoz0https://orcid.org/0000-0002-0644-5938Gokhan Dindis1https://orcid.org/0000-0001-5642-7212Department of Electrical and Electronics Engineering, Faculty of Engineering and Architecture, Eskişehir Osmangazi Üniversitesi, Odunpazarı, Eskişehir, TürkiyeDepartment of Electrical and Electronics Engineering, Faculty of Engineering and Architecture, Eskişehir Osmangazi Üniversitesi, Odunpazarı, Eskişehir, TürkiyeWith the development of technology, target and object recognition issues are becoming increasingly important day by day. In this article, studies on ultrasonic signal processing and object recognition techniques in the literature were examined. An integrated system design that includes ultrasonic signal data obtained from different objects has been designed to contribute to the literature. With this system design, a new method is proposed that includes signal pre-processing, feature extraction and classification processes. In addition, different machine learning and artificial neural networks algorithms are used to organize and classify the data obtained from ultrasonic signals, to obtain high-performance classification rates and to achieve comprehensive object recognition. As a result of the study carried out in this context, the data set obtained from different types of objects at different distances and angles was arranged and passed through various pre-processing and statistical feature extraction methods. In conclusion, a high-performance classification rate of 97.8% using the random forest algorithm and 99.10% using the artificial neural networks algorithm was obtained during the classification phase.https://ieeexplore.ieee.org/document/11006063/Artificial neural networksobject classificationsignal processingultrasonic sensors |
| spellingShingle | Ahmet Karagoz Gokhan Dindis An End to End Solution for Object Classification Using Artificial Neural Networks and Ultrasonic-Based Sensors IEEE Access Artificial neural networks object classification signal processing ultrasonic sensors |
| title | An End to End Solution for Object Classification Using Artificial Neural Networks and Ultrasonic-Based Sensors |
| title_full | An End to End Solution for Object Classification Using Artificial Neural Networks and Ultrasonic-Based Sensors |
| title_fullStr | An End to End Solution for Object Classification Using Artificial Neural Networks and Ultrasonic-Based Sensors |
| title_full_unstemmed | An End to End Solution for Object Classification Using Artificial Neural Networks and Ultrasonic-Based Sensors |
| title_short | An End to End Solution for Object Classification Using Artificial Neural Networks and Ultrasonic-Based Sensors |
| title_sort | end to end solution for object classification using artificial neural networks and ultrasonic based sensors |
| topic | Artificial neural networks object classification signal processing ultrasonic sensors |
| url | https://ieeexplore.ieee.org/document/11006063/ |
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