Obstacle detection using millimeter-wave radar and its visualization on image sequence

2004 ◽  
Author(s):  
S. Sugimoto ◽  
H. Tateda ◽  
H. Takahashi ◽  
M. Okutomi
Keyword(s):  
Millimeter Wave ◽  
Image Sequence ◽  
Obstacle Detection ◽  
Millimeter Wave Radar ◽  
Wave Radar

Obstacle detection and tracking by millimeter wave RADAR

2004 ◽  
Vol 37 (8) ◽  
pp. 322-327 ◽  
Author(s):  
Christophe Blanc ◽  
Romuald Aufrère ◽  
Laurent Malaterre ◽  
Jean Gallice ◽  
Joseph Alizon
Keyword(s):  
Millimeter Wave ◽  
Obstacle Detection ◽  
Millimeter Wave Radar ◽  
Detection And Tracking ◽  
Wave Radar

Radar and vision fusion for the real-time obstacle detection and identification

2019 ◽  
Vol 46 (3) ◽  
pp. 391-395 ◽  
Author(s):  
Xinyu Zhang ◽  
Mo Zhou ◽  
Peng Qiu ◽  
Yi Huang ◽  
Jun Li
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Real Time ◽  
Millimeter Wave ◽  
Obstacle Detection ◽  
Content Type ◽  
Millimeter Wave Radar ◽  
Bounding Box ◽  
Detection And Identification ◽  
Wave Radar

Purpose The purpose of this paper is the presentation and research of a novel sensor fusion-based system for obstacle detection and identification, which uses the millimeter-wave radar to detect the position and velocity of the obstacle. Afterwards, the image processing module uses the bounding box regression algorithm in deep learning to precisely locate and identify the obstacles. Design/methodology/approach Unlike the traditional algorithms that use radar and vision to detect obstacles separately, the purposed method of this paper uses radar to determine the approximate location of obstacles and then uses bounding box regression to achieve accurate positioning and recognition. First, the information of the obstacles can be acquired by the millimeter-wave radar, and the effective target is extracted by filtering the data. Then, use coordinate system conversion and camera parameter calibration to project the effective target to the image plane, and generate the region of interest (ROI). Finally, based on image processing and machine learning techniques, the vehicle targets in the ROI are detected and tracked. Findings The millimeter wave is used to determine the presence of an obstacle, and the deep learning algorithm of the image is combined to determine the shape and the class of the obstacle. The experimental results indicate that the detection rate of this method is up to 91.6 per cent, which can better implement the perception of the environment in front of the vehicle. Originality/value The originality is based on the combination of millimeter-wave sensors and deep learning. Using the bounding box regression algorithm in RCNN, the ROI detected by radar is analyzed to realize real-time obstacle detection and recognition. This method does not require processing the entire image, greatly reducing the amount of data processing and improving the efficiency of the algorithm.

scholarly journals Integrating Millimeter Wave Radar with a Monocular Vision Sensor for On-Road Obstacle Detection Applications

Sensors ◽  
2011 ◽  
Vol 11 (9) ◽  
pp. 8992-9008 ◽  
Author(s):  
Tao Wang ◽  
Nanning Zheng ◽  
Jingmin Xin ◽  
Zheng Ma
Keyword(s):  
Millimeter Wave ◽  
Obstacle Detection ◽  
Monocular Vision ◽  
Vision Sensor ◽  
Millimeter Wave Radar ◽  
Wave Radar
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