Road target detection and association method based on millimeter wave radar and vision

Abstract

The invention discloses a road target detection and association method based on millimeter-wave radar and vision. The method comprises the following steps: 1, installing the millimeter-wave radar and a camera on a vehicle; 2, constructing an improved YOLOv3 network which comprises a main body network and three feature prediction layers, and constructing a loss function and training the network based on DIOU so as to obtain a trained visual detection model; 3, configuring parameters of the millimeter-wave radar, acquiring CAN signals by using the configured millimeter-wave radar, processing the CAN signals, outputting preset frames with different target preset lengths and widths for the millimeter-wave radar, and projecting the preset frames back to a pixel coordinate system; and 4, calculating cost values of the radar target preset frame and the visual target frame through a DIOU-based cost calculation method, and setting a reasonable threshold value for optimization solution through a Hungary algorithm to obtain a global optimal matching result. According to the method, the correlation precision of the millimeter wave radar detection target and the visual detection target can be improved, so that richer target information can be obtained.

Description

technical field [0001] The invention relates to the field of vehicle intelligent detection equipment, in particular to a road target detection and association method based on millimeter wave radar and vision. Background technique [0002] At present, smart cars are equipped with various advanced sensors and communication devices, using deep learning, 5G technology, positioning technology, etc., for safer and more comfortable driving for passengers, and finally realize true driverless driving. [0003] Lidar is expensive, has high resolution, good imaging effect, accurate ranging, and can clearly obtain the size and outline of the target. in different weather conditions. However, its resolution is low, the target classification ability is poor, and it is difficult to obtain the target size and complete outline; the camera is not expensive, and the obtained image information is rich. With the popularity of deep learning, the effect of using the camera for target detection is ...

AI Technical Summary

Problems solved by technology

[0005] Aiming at the problems of low accuracy and weak robustness existing in the prior art, the present invention provides a road target detection and association method based on millimeter-wave radar and vision, in order to obtain more abundant target information and improve the Correlation accuracy between wave radar detection target and visual detection target, so as to solve the problem of poor detection effect and low robustness of single sensor perception

Images