scholarly journals Fully Convolutional Neural Networks for Automotive Radar Interference Mitigation

2020 ◽  
Author(s):  
Nicolae-Catalin Ristea ◽  
Andrei Anghel ◽  
Radu Tudor Ionescu
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Interference Mitigation ◽  
Automotive Radar ◽  
Fully Convolutional Neural Networks

scholarly journals Fully Convolutional Neural Networks for Automotive Radar Interference Mitigation

2020 ◽  
Author(s):  
Nicolae-Catalin Ristea ◽  
Andrei Anghel ◽  
Radu Tudor Ionescu
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Large Scale ◽  
Interference Mitigation ◽  
Continuous Wave ◽  
Automotive Radar ◽  
Driver Assistance Systems ◽  
Data Set ◽  
Radar Sensors ◽  
Fully Convolutional Neural Networks

The interest of the automotive industry has progressively focused on subjects related to driver assistance systems as well as autonomous cars. In order to achieve remarkable results, cars combine a variety of sensors to perceive their surroundings robustly. Among them, radar sensors are indispensable because of their independence of light conditions and the possibility to directly measure velocity. However, radar interference is an issue that becomes prevalent with the increasing amount of radar systems in automotive scenarios. In this paper, we address this issue for frequency modulated continuous wave (FMCW) radars with fully convolutional neural networks (FCNs), a state-of-the-art deep learning technique. We propose two FCNs that take spectrograms of the beat signals as input, and provide the corresponding clean range profiles as output. We propose two architectures for interference mitigation which outperform the classical zeroing technique. Moreover, considering the lack of databases for this task, we release as open source a large scale data set that closely replicates real world automotive scenarios for single-interference cases, allowing others to compare objectively their future work in this domain. The data set is available for download at: http://github.com/ristea/arim.

scholarly journals Fully Convolutional Neural Networks for Automotive Radar Interference Mitigation

2020 ◽  
Author(s):  
Nicolae-Catalin Ristea ◽  
Andrei Anghel ◽  
Radu Tudor Ionescu
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Interference Mitigation ◽  
Continuous Wave ◽  
Driver Assistance Systems ◽  
Data Set ◽  
Radar Sensors ◽  
Radar Systems ◽  
Fully Convolutional Neural Networks ◽  
Autonomous Cars

The interest of the automotive industry has progressively focused on subjects related to driver assistance systems as well as autonomous cars. In order to achieve remarkable results, cars combine a variety of sensors to perceive their surroundings robustly. Among them, radar sensors are indispensable because of their independence of light conditions and the possibility to directly measure velocity. However, radar interference is an issue that becomes prevalent with the increasing amount of radar systems in automotive scenarios. In this paper, we address this issue for frequency modulated continuous wave (FMCW) radars with fully convolutional neural networks (FCNs), a state-of-the-art deep learning technique. The interest of the automotive industry has progressively focused on subjects related to driver assistance systems as well as autonomous cars. Cars combine a variety of sensors to perceive their surroundings robustly. Among them, radar sensors are indispensable because of their independence of lighting conditions and the possibility to directly measure velocity. However, radar interference is an issue that becomes prevalent with the increasing amount of radar systems in automotive scenarios. In this paper, we address this issue for frequency modulated continuous wave (FMCW) radars with fully convolutional neural networks (FCNs), a state-of-the-art deep learning technique. We propose two FCNs that take spectrograms of the beat signals as input, and provide the corresponding clean range profiles as output. We propose two architectures for interference mitigation which outperform the classical zeroing technique. Moreover, considering the lack of databases for this task, we release as open source a large scale data set that closely replicates real world automotive scenarios for single-interference cases, allowing others to objectively compare their future work in this domain. The data set is available for download at: http://github.com/ristea/arim.

scholarly journals Fully Convolutional Neural Networks for Automotive Radar Interference Mitigation

2020 ◽  
Author(s):  
Nicolae-Catalin Ristea ◽  
Andrei Anghel ◽  
Radu Tudor Ionescu
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Interference Mitigation ◽  
Continuous Wave ◽  
Driver Assistance Systems ◽  
Data Set ◽  
Radar Sensors ◽  
Radar Systems ◽  
Fully Convolutional Neural Networks ◽  
Autonomous Cars

The interest of the automotive industry has progressively focused on subjects related to driver assistance systems as well as autonomous cars. In order to achieve remarkable results, cars combine a variety of sensors to perceive their surroundings robustly. Among them, radar sensors are indispensable because of their independence of light conditions and the possibility to directly measure velocity. However, radar interference is an issue that becomes prevalent with the increasing amount of radar systems in automotive scenarios. In this paper, we address this issue for frequency modulated continuous wave (FMCW) radars with fully convolutional neural networks (FCNs), a state-of-the-art deep learning technique. The interest of the automotive industry has progressively focused on subjects related to driver assistance systems as well as autonomous cars. Cars combine a variety of sensors to perceive their surroundings robustly. Among them, radar sensors are indispensable because of their independence of lighting conditions and the possibility to directly measure velocity. However, radar interference is an issue that becomes prevalent with the increasing amount of radar systems in automotive scenarios. In this paper, we address this issue for frequency modulated continuous wave (FMCW) radars with fully convolutional neural networks (FCNs), a state-of-the-art deep learning technique. We propose two FCNs that take spectrograms of the beat signals as input, and provide the corresponding clean range profiles as output. We propose two architectures for interference mitigation which outperform the classical zeroing technique. Moreover, considering the lack of databases for this task, we release as open source a large scale data set that closely replicates real world automotive scenarios for single-interference cases, allowing others to objectively compare their future work in this domain. The data set is available for download at: http://github.com/ristea/arim.

scholarly journals Fully Convolutional Neural Networks for Automotive Radar Interference Mitigation

2020 ◽  
Author(s):  
Nicolae-Catalin Ristea ◽  
Andrei Anghel ◽  
Radu Tudor Ionescu
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Interference Mitigation ◽  
Continuous Wave ◽  
Driver Assistance Systems ◽  
Data Set ◽  
Radar Sensors ◽  
Radar Systems ◽  
Fully Convolutional Neural Networks ◽  
Autonomous Cars

The interest of the automotive industry has progressively focused on subjects related to driver assistance systems as well as autonomous cars. In order to achieve remarkable results, cars combine a variety of sensors to perceive their surroundings robustly. Among them, radar sensors are indispensable because of their independence of light conditions and the possibility to directly measure velocity. However, radar interference is an issue that becomes prevalent with the increasing amount of radar systems in automotive scenarios. In this paper, we address this issue for frequency modulated continuous wave (FMCW) radars with fully convolutional neural networks (FCNs), a state-of-the-art deep learning technique. The interest of the automotive industry has progressively focused on subjects related to driver assistance systems as well as autonomous cars. Cars combine a variety of sensors to perceive their surroundings robustly. Among them, radar sensors are indispensable because of their independence of lighting conditions and the possibility to directly measure velocity. However, radar interference is an issue that becomes prevalent with the increasing amount of radar systems in automotive scenarios. In this paper, we address this issue for frequency modulated continuous wave (FMCW) radars with fully convolutional neural networks (FCNs), a state-of-the-art deep learning technique. We propose two FCNs that take spectrograms of the beat signals as input, and provide the corresponding clean range profiles as output. We propose two architectures for interference mitigation which outperform the classical zeroing technique. Moreover, considering the lack of databases for this task, we release as open source a large scale data set that closely replicates real world automotive scenarios for single-interference cases, allowing others to objectively compare their future work in this domain. The data set is available for download at: http://github.com/ristea/arim.

scholarly journals Fully convolutional neural networks for polyp segmentation in colonoscopy

2017 ◽  
Author(s):  
Patrick Brandao ◽  
Evangelos Mazomenos ◽  
Gastone Ciuti ◽  
Renato Caliò ◽  
Federico Bianchi ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Fully Convolutional Neural Networks
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