Object Detection on Radar Imagery for Autonomous Driving Using Deep Neural Networks
Object Detection on Radar Imagery for Autonomous Driving Using Deep Neural Networks
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DOI：
10.1109/eurad48048.2021.00041
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发表时间：
2021-01
期刊：
影响因子：
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通讯作者：
Ana Stroescu;L. Daniel;D. Phippen;M. Cherniakov;M. Gashinova
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文献类型：
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作者：
Ana Stroescu;L. Daniel;D. Phippen;M. Cherniakov;M. Gashinova
该文针对成像雷达面临的挑战，提出了一种解决方案，以响应传统上被认为是杂波的雷达图像中目标检测和分类的自主性要求。与传统的雷达相关背景下的目标检测方法不同，本文对目标检测方法进行了新的定义。本文首次将这种基于深度神经网络的目标检测方法应用于室外雷达图像和室内受控环境下拍摄的图像。利用快速R-CNN和SSD两种检测器进行了目标检测，评估结果证明该方法可以成功地应用于雷达图像的自主应用。
This paper presents a solution to the current challenges of the imaging radar to respond the demands of autonomy for detection and classification of targets in radar imagery, which traditionally has been considered as clutter. The proposed object detection method is defined in a new way, as opposed to the traditional object detection methods in the radar related contexts. The current paper presents the first application of this novel approach, based on deep neural networks for object detection, on outdoor radar images, as well as indoor images taken in controlled environment. Object detection was performed using two detectors, Faster R-CNN and SSD and the evaluation proved that this method can be successfully used on radar imagery for autonomous applications.