A target detection method for vehicle vision system
A target detection and vision system technology, which is applied in the target detection field of vehicle vision systems, can solve the problems of large environmental changes and low performance accuracy, and achieve the effect of shortening the target detection time and improving the target detection performance
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Publication Date
- 2020-10-09
Smart Images

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Abstract
Description
technical field
[0001] The invention relates to the technical field of image processing, in particular to a target detection method for a vehicle-mounted vision system. Background technique
[0002] The vehicle vision system has a very broad application prospect in the fields of intelligent transportation, automatic driving, assisted driving and intelligent robots. From a technical point of view, the target detection method based on vehicle vision mainly includes feature extraction, classifier design, target detection and detection result suppression.
[0003] The on-vehicle vision system captures the natural scene images in front of the road through the front-view camera installed on the driving vehicle, and inputs them into the target detection module in the system to complete the processing. Due to the complexity and changeability of natural scenes, it is more challenging than target detection in unnatural scenes. The difficulties mainly include susceptibility to illumin...
Examples
Embodiment Construction
[0040] Preferred embodiments of the present invention will be specifically described below in conjunction with the accompanying drawings, wherein the accompanying drawings constitute a part of the application and are used together with the embodiments of the present invention to explain the principles of the present invention.
[0041] A specific embodiment of the present invention discloses a target detection method for a vehicle-mounted vision system; figure 1 shown, including the following steps:
[0042] Step S1, divide the target into N intervals according to the posture presented by the target in the visual image, construct a training sample library composed of N training subsets, and extract an approximate channel feature pyramid from the training sample library samples;
[0043] Step S101, dividing the training sample library composed of multi-angle target images into N training sample subsets;
[0044] Divide the view angle presented by the target into N intervals at...