Short-Wave Infrared (SWIR) Imaging for Robust Material Classification: Overcoming Limitations of Visible Spectrum Data
This paper presents a novel approach to material classification using short-wave infrared (SWIR) imaging, aimed at applications where differentiating visually similar objects based on material properties is essential, such as in autonomous driving. Traditional vision systems, relying on visible spec...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-11-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/14/23/11049 |
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| author | Hanbin Song Sanghyeop Yeo Youngwan Jin Incheol Park Hyeongjin Ju Yagiz Nalcakan Shiho Kim |
| author_facet | Hanbin Song Sanghyeop Yeo Youngwan Jin Incheol Park Hyeongjin Ju Yagiz Nalcakan Shiho Kim |
| author_sort | Hanbin Song |
| collection | DOAJ |
| description | This paper presents a novel approach to material classification using short-wave infrared (SWIR) imaging, aimed at applications where differentiating visually similar objects based on material properties is essential, such as in autonomous driving. Traditional vision systems, relying on visible spectrum imaging, struggle to distinguish between objects with similar appearances but different material compositions. Our method leverages SWIR’s distinct reflectance characteristics, particularly for materials containing moisture, and demonstrates a significant improvement in accuracy. Specifically, SWIR data achieved near-perfect classification results with an accuracy of 99% for distinguishing real from artificial objects, compared to 77% with visible spectrum data. In object detection tasks, our SWIR-based model achieved a mean average precision (mAP) of 0.98 for human detection and up to 1.00 for other objects, demonstrating its robustness in reducing false detections. This study underscores SWIR’s potential to enhance object recognition and reduce ambiguity in complex environments, offering a valuable contribution to material-based object recognition in autonomous driving, manufacturing, and beyond. |
| format | Article |
| id | doaj-art-8bc2d7b6a7d3454d8254b7254100f428 |
| institution | DOAJ |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-8bc2d7b6a7d3454d8254b7254100f4282025-08-20T02:50:15ZengMDPI AGApplied Sciences2076-34172024-11-0114231104910.3390/app142311049Short-Wave Infrared (SWIR) Imaging for Robust Material Classification: Overcoming Limitations of Visible Spectrum DataHanbin Song0Sanghyeop Yeo1Youngwan Jin2Incheol Park3Hyeongjin Ju4Yagiz Nalcakan5Shiho Kim6School of Integrated Technology, Yonsei University, Incheon 21983, Republic of KoreaSchool of Integrated Technology, Yonsei University, Incheon 21983, Republic of KoreaSchool of Integrated Technology, Yonsei University, Incheon 21983, Republic of KoreaSchool of Integrated Technology, Yonsei University, Incheon 21983, Republic of KoreaSchool of Integrated Technology, Yonsei University, Incheon 21983, Republic of KoreaSchool of Integrated Technology, Yonsei University, Incheon 21983, Republic of KoreaSchool of Integrated Technology, Yonsei University, Incheon 21983, Republic of KoreaThis paper presents a novel approach to material classification using short-wave infrared (SWIR) imaging, aimed at applications where differentiating visually similar objects based on material properties is essential, such as in autonomous driving. Traditional vision systems, relying on visible spectrum imaging, struggle to distinguish between objects with similar appearances but different material compositions. Our method leverages SWIR’s distinct reflectance characteristics, particularly for materials containing moisture, and demonstrates a significant improvement in accuracy. Specifically, SWIR data achieved near-perfect classification results with an accuracy of 99% for distinguishing real from artificial objects, compared to 77% with visible spectrum data. In object detection tasks, our SWIR-based model achieved a mean average precision (mAP) of 0.98 for human detection and up to 1.00 for other objects, demonstrating its robustness in reducing false detections. This study underscores SWIR’s potential to enhance object recognition and reduce ambiguity in complex environments, offering a valuable contribution to material-based object recognition in autonomous driving, manufacturing, and beyond.https://www.mdpi.com/2076-3417/14/23/11049material classificationshort-wave infraredmulti-spectral imagingmulti-modal object detectionautonomous driving safety |
| spellingShingle | Hanbin Song Sanghyeop Yeo Youngwan Jin Incheol Park Hyeongjin Ju Yagiz Nalcakan Shiho Kim Short-Wave Infrared (SWIR) Imaging for Robust Material Classification: Overcoming Limitations of Visible Spectrum Data Applied Sciences material classification short-wave infrared multi-spectral imaging multi-modal object detection autonomous driving safety |
| title | Short-Wave Infrared (SWIR) Imaging for Robust Material Classification: Overcoming Limitations of Visible Spectrum Data |
| title_full | Short-Wave Infrared (SWIR) Imaging for Robust Material Classification: Overcoming Limitations of Visible Spectrum Data |
| title_fullStr | Short-Wave Infrared (SWIR) Imaging for Robust Material Classification: Overcoming Limitations of Visible Spectrum Data |
| title_full_unstemmed | Short-Wave Infrared (SWIR) Imaging for Robust Material Classification: Overcoming Limitations of Visible Spectrum Data |
| title_short | Short-Wave Infrared (SWIR) Imaging for Robust Material Classification: Overcoming Limitations of Visible Spectrum Data |
| title_sort | short wave infrared swir imaging for robust material classification overcoming limitations of visible spectrum data |
| topic | material classification short-wave infrared multi-spectral imaging multi-modal object detection autonomous driving safety |
| url | https://www.mdpi.com/2076-3417/14/23/11049 |
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