Deep CNNs for Object Detection Using Passive Millimeter Sensors
| Autor: | Rafael Molina, Nicolás Pérez de la Blanca, Santiago Lopez-Tapia |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Computer science
business.industry Deep learning Feature extraction 02 engineering and technology Image segmentation Object detection 0202 electrical engineering electronic engineering information engineering Media Technology 020201 artificial intelligence & image processing Millimeter Computer vision Noise (video) Artificial intelligence Electrical and Electronic Engineering Image sensor business |
| Zdroj: | IEEE Transactions on Circuits and Systems for Video Technology. 29:2580-2589 |
| ISSN: | 1558-2205 1051-8215 |
| DOI: | 10.1109/tcsvt.2017.2774927 |
| Popis: | Passive millimeter wave images (PMMWIs) can be used to detect and localize objects concealed under clothing. Unfortunately, the quality of the acquired images and the unknown position, shape, and size of the hidden objects render these tasks challenging. In this paper, we discuss a deep learning approach to this detection/localization problem. The effect of the nonstationary acquisition noise on different architectures is analyzed and discussed. A comparison with shallow architectures is also presented. The achieved detection accuracy defines a new state of the art in object detection on PMMWIs. The low computational training and testing costs of the solution allow its use in real-time applications. |
| Databáze: | OpenAIRE |
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