A method for integrating driving scene target recognition and traveling area segmentation

Abstract

The invention discloses a driving scene target recognition and traveling area segmentation integration method. The method comprises the following steps: S1, a shared feature module obtains parameter configuration information and image information of an input vehicle vision system; S2, the target detection network module outputs the target category, the abscissa of the upper left corner of the target, the ordinate of the upper left corner of the target, the width of the target and the height of the target according to the three target detection features of different sizes inputted in the targetdetection ROI area; S3, carrying out confidence threshold filtering and maximum suppression on that target category, the abscissa of the upper left corn of the target, the ordinate of the upper leftcorner of the target, the width of the target and the height of the target by the target detection network module, and merging and outputting a target detection list; S4, the scene segmentation network module outputting a single-channel passable area binary output map corresponding to the scene segmentation feature according to three scene segmentation features of different sizes inputted in the scene segmentation ROI area. By adopting the invention, the robustness and the accuracy are greatly improved.

Description

technical field [0001] The invention relates to a vehicle vision system, in particular to a method for integrating driving scene object recognition and drivable region segmentation in the vehicle vision system. Background technique [0002] The perception functions of existing commercial vehicle vision systems mainly include the detection and recognition of pedestrians, vehicles, traffic signs and structured road marking lines. Most of its algorithm levels are based on traditional visual processing and its learning methods, including basic image feature operators, Hough transform, adaboost or SVM classifiers, etc. The effect of this kind of method depends on the design of the image feature description operator, and the robustness and portability of the application are poor. Its limitations and application difficulties lie in that different types of target detection, such as pedestrians, vehicles, and traffic signs, require the design of different image feature description o...

AI Technical Summary

Problems solved by technology

The effect of this type of method depends on the design of the image feature description operator, and the robustness and portability of the application are poor

Its limitations and application difficulties lie in that different types of target detection, such as pedestrians, vehicles, and traffic signs, require the design of different image feature description operators. The algorithmic target detection architecture and methods for day and night need to be adjusted separately. For unstructured road boundaries Difficult to identify etc.

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