Material Segmentation Using 1-D Convolutional Neural Network With Transient Histogram
This study introduces a method for material classification using transient histograms obtained via a single-photon avalanche diode (SPAD) sensor. Temporal resolution in optical sensing plays a crucial role in material classification and surface segmentation, particularly for distinguishing materials...
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| Format: | Article |
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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/10942358/ |
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| author | Yohanssen Pratama Kazuya Kitano Takahiro Kushida Yuki Fujimura Takuya Funatomi Yasuhiro Mukaigawa |
| author_facet | Yohanssen Pratama Kazuya Kitano Takahiro Kushida Yuki Fujimura Takuya Funatomi Yasuhiro Mukaigawa |
| author_sort | Yohanssen Pratama |
| collection | DOAJ |
| description | This study introduces a method for material classification using transient histograms obtained via a single-photon avalanche diode (SPAD) sensor. Temporal resolution in optical sensing plays a crucial role in material classification and surface segmentation, particularly for distinguishing materials with similar visual properties. In this study, SPAD sensors were utilized to capture transient histograms with temporal resolutions ranging from 13 picoseconds to 208 picoseconds, enabling precise extraction of temporal signatures for various materials. A comparative evaluation of classification techniques, including one-dimensional convolutional neural networks (1-D CNN), random forest (RF), support vector classifier (SVC), and k-nearest neighbors (KNN), was conducted to assess the impact of temporal resolution and exposure time on classification accuracy. 1-D CNN achieved the highest classification accuracy of 99.25% at a temporal resolution of 13 ps and an exposure time of 0.09 s, significantly outperforming other methods. Additionally, the proposed SPAD-based system was evaluated for material segmentation on non-planar surfaces. In a real-world experiment, 1-D CNN achieved an overall accuracy of 87.5% in differentiating visually similar materials, demonstrating the effectiveness of transient histograms for material classification where conventional RGB-based methods fail. These findings highlight the potential of SPAD sensors combined with advanced classification techniques to enhance material classification and segmentation, providing a versatile framework for applications in robotics, computer vision, and optical sensing. |
| format | Article |
| id | doaj-art-c29ad52d8b2b4772b607a5bbda5f6eea |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-c29ad52d8b2b4772b607a5bbda5f6eea2025-08-20T03:17:46ZengIEEEIEEE Access2169-35362025-01-0113592955930810.1109/ACCESS.2025.355477610942358Material Segmentation Using 1-D Convolutional Neural Network With Transient HistogramYohanssen Pratama0https://orcid.org/0000-0001-6073-9016Kazuya Kitano1https://orcid.org/0000-0001-6430-6963Takahiro Kushida2https://orcid.org/0000-0002-6965-9781Yuki Fujimura3Takuya Funatomi4https://orcid.org/0000-0001-5588-5932Yasuhiro Mukaigawa5https://orcid.org/0000-0001-8689-3724Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Nara, JapanGraduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Nara, JapanCollege of Information Science and Engineering, Ritsumeikan University, Osaka, Ibaraki, JapanGraduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Nara, JapanGraduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Nara, JapanGraduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Nara, JapanThis study introduces a method for material classification using transient histograms obtained via a single-photon avalanche diode (SPAD) sensor. Temporal resolution in optical sensing plays a crucial role in material classification and surface segmentation, particularly for distinguishing materials with similar visual properties. In this study, SPAD sensors were utilized to capture transient histograms with temporal resolutions ranging from 13 picoseconds to 208 picoseconds, enabling precise extraction of temporal signatures for various materials. A comparative evaluation of classification techniques, including one-dimensional convolutional neural networks (1-D CNN), random forest (RF), support vector classifier (SVC), and k-nearest neighbors (KNN), was conducted to assess the impact of temporal resolution and exposure time on classification accuracy. 1-D CNN achieved the highest classification accuracy of 99.25% at a temporal resolution of 13 ps and an exposure time of 0.09 s, significantly outperforming other methods. Additionally, the proposed SPAD-based system was evaluated for material segmentation on non-planar surfaces. In a real-world experiment, 1-D CNN achieved an overall accuracy of 87.5% in differentiating visually similar materials, demonstrating the effectiveness of transient histograms for material classification where conventional RGB-based methods fail. These findings highlight the potential of SPAD sensors combined with advanced classification techniques to enhance material classification and segmentation, providing a versatile framework for applications in robotics, computer vision, and optical sensing.https://ieeexplore.ieee.org/document/10942358/Material classificationsingle photon avalanche diodesurface segmentationtemporal resolutiontransient histogram1-D convolutional neural network |
| spellingShingle | Yohanssen Pratama Kazuya Kitano Takahiro Kushida Yuki Fujimura Takuya Funatomi Yasuhiro Mukaigawa Material Segmentation Using 1-D Convolutional Neural Network With Transient Histogram IEEE Access Material classification single photon avalanche diode surface segmentation temporal resolution transient histogram 1-D convolutional neural network |
| title | Material Segmentation Using 1-D Convolutional Neural Network With Transient Histogram |
| title_full | Material Segmentation Using 1-D Convolutional Neural Network With Transient Histogram |
| title_fullStr | Material Segmentation Using 1-D Convolutional Neural Network With Transient Histogram |
| title_full_unstemmed | Material Segmentation Using 1-D Convolutional Neural Network With Transient Histogram |
| title_short | Material Segmentation Using 1-D Convolutional Neural Network With Transient Histogram |
| title_sort | material segmentation using 1 d convolutional neural network with transient histogram |
| topic | Material classification single photon avalanche diode surface segmentation temporal resolution transient histogram 1-D convolutional neural network |
| url | https://ieeexplore.ieee.org/document/10942358/ |
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