Radar-based Materials Classification Using Deep Wavelet Scattering Transform: A Comparison of Centimeter vs. Millimeter Wave Units
| Autor: | Rami Khushaba, Andrew Hill |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: |
Signal Processing (eess.SP)
FOS: Computer and information sciences Computer Science - Machine Learning Control and Optimization Mechanical Engineering Biomedical Engineering Computer Science Applications Machine Learning (cs.LG) Human-Computer Interaction Computer Science - Robotics Artificial Intelligence Control and Systems Engineering FOS: Electrical engineering electronic engineering information engineering Computer Vision and Pattern Recognition Electrical Engineering and Systems Science - Signal Processing Robotics (cs.RO) |
| Popis: | Radar-based materials detection received significant attention in recent years for its potential inclusion in consumer and industrial applications like object recognition for grasping and manufacturing quality assurance and control. Several radar publications were developed for material classification under controlled settings with specific materials' properties and shapes. Recent literature has challenged the earlier findings on radars-based materials classification claiming that earlier solutions are not easily scaled to industrial applications due to a variety of real-world issues. Published experiments on the impact of these factors on the robustness of the extracted radar-based traditional features have already demonstrated that the application of deep neural networks can mitigate, to some extent, the impact to produce a viable solution. However, previous studies lacked an investigation of the usefulness of lower frequency radar units, specifically 6 pages, 8 figures, accepted IEEE in Robotics and Automation Letters c. January 2022 associated video: https://www.youtube.com/watch?v=Mfohzvf7iuA |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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