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300 GHz radar object recognition based on deep neural networks and transfer learning
IET Radar Sonar and Navigation ( IF 1.7 ) Pub Date : 2020-09-17 , DOI: 10.1049/iet-rsn.2019.0601 Marcel Sheeny 1 , Andrew Wallace 1 , Sen Wang 1
IET Radar Sonar and Navigation ( IF 1.7 ) Pub Date : 2020-09-17 , DOI: 10.1049/iet-rsn.2019.0601 Marcel Sheeny 1 , Andrew Wallace 1 , Sen Wang 1
Affiliation
For high-resolution scene mapping and object recognition, optical technologies such as cameras and LiDAR are the sensors of choice. However, for future vehicle autonomy and driver assistance in adverse weather conditions, improvements in automotive radar technology and the development of algorithms and machine learning for robust mapping and recognition are essential. In this study, the authors describe a methodology based on deep neural networks to recognise objects in 300 GHz radar images using the returned power data only, investigating robustness to changes in range, orientation and different receivers in a laboratory environment. As the training data is limited, they have also investigated the effects of transfer learning. As a necessary first step before road trials, they have also considered detection and classification in multiple object scenes.
中文翻译:
基于深度神经网络和传递学习的300 GHz雷达目标识别
对于高分辨率场景映射和对象识别,光学技术(例如摄像机和LiDAR)是首选传感器。但是,对于未来在恶劣天气条件下的车辆自动驾驶和驾驶员协助,汽车雷达技术的改进以及用于鲁棒映射和识别的算法和机器学习的发展至关重要。在这项研究中,作者描述了一种基于深度神经网络的方法,该方法仅使用返回的功率数据即可识别300 GHz雷达图像中的物体,从而研究了在实验室环境中对范围,方向和不同接收器变化的鲁棒性。由于培训数据有限,他们还研究了转移学习的效果。作为路试之前的必要第一步,
更新日期:2020-09-18
中文翻译:
基于深度神经网络和传递学习的300 GHz雷达目标识别
对于高分辨率场景映射和对象识别,光学技术(例如摄像机和LiDAR)是首选传感器。但是,对于未来在恶劣天气条件下的车辆自动驾驶和驾驶员协助,汽车雷达技术的改进以及用于鲁棒映射和识别的算法和机器学习的发展至关重要。在这项研究中,作者描述了一种基于深度神经网络的方法,该方法仅使用返回的功率数据即可识别300 GHz雷达图像中的物体,从而研究了在实验室环境中对范围,方向和不同接收器变化的鲁棒性。由于培训数据有限,他们还研究了转移学习的效果。作为路试之前的必要第一步,