Target detection method and target detection system of visual radar spatial and temporal information fusion

Abstract

The invention discloses a target detection method and a target detection system of visual radar spatial and temporal information fusion. The target detection system comprises an acquisition unit, a sampling unit, a superposition unit, a model building unit and an execution unit. The acquisition unit is used for collecting RGB image data and 3D point cloud data to calculate discretized LIDAR depthmap in grayscale; the sampling unit is used for up-sampling and densifying the LIDAR depth map so that the RGB image and the data form of the LIDAR depth map are unified and corresponded to each otherone by one; the superposition unit is used for combining the RGB image and the LIDAR depth map into an RGB-LIDAR picture and superposing the RGB-LIDAR pictures which are continuously collected for multiple times to obtain a superposed RGB-LIDAR picture, wherein the number of the continuous collection of the RGB-LIDAR pictures is equal to or more than 1; the model building unit is used for establishing an RGB-LIDAR data set for the multiple superposed RGB-LIDAR pictures to enter the deep learning network for training and learning and establish a classification model; the execution unit is usedfor taking corresponding decisions according to target analysis results from the classification model. Consequently, the effects of long-distance recognition and high classification accuracy are achieved.

Description

technical field [0001] The invention relates to a target detection method and system based on the fusion of vision and radar information. The depth information picture is fused according to the radar information and the picture visual information, and the convolutional neural network is used to learn and establish a classification model according to the depth picture. Background technique [0002] At present, some unmanned vehicles have appeared, which can drive automatically without driving, and can replace manual work such as delivery, pick-up, cleaning or measurement. Unmanned vehicles detect roads and obstacles by setting sensors, but they cannot identify various traffic information on the road, such as vehicles and pedestrians, which may easily cause traffic accidents. [0003] In order to solve the above problems, the Chinese patent document CN105975915A discloses a vehicle multi-parameter recognition method based on a multi-task convolutional neural network, the input...

AI Technical Summary

Problems solved by technology

[0004] The technical problem to be solved by the present invention is that the convolutional neural network in the prior art is trained through RGB-D, the recognition accuracy is low, and the recognit

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