A Lane Line Detection Method Based on Local Line Segment Pattern Features
A lane line detection and pattern feature technology, applied in the field of pattern recognition, can solve the problems of high image quality requirements and low detection accuracy of curved roads and urban roads, and achieves the goal of being beneficial to the detection of straight lines and curves, and improving the anti-interference of shadows. performance, the effect of avoiding noise interference
Active Publication Date: 2021-06-08
GUANGXI UNIV
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[0004] In view of this, the present invention provides a lane line detection method based on local line segment pattern features, which solves the problem in the prior art that road images are seriously disturbed by shadows, lanes usually have curves and some roads are bumpy When the image quality of urban roads is too high and the detection accuracy is low
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[0027] Such as Figure 1-7 As shown, the embodiment of the present invention discloses a lane line detection method based on local line segment pattern features, including the following steps:
[0028] S1, set up an image library, specifically: S11, collect images; S12, convert the image to grayscale; S13, divide the image after grayscale conversion into an original road image library and a test road image library; S14, convert the original road image The images in the library are cut and marked to generate a regional road database.
[0029] More specifically, this step S14 is: S141, image cutting: successively cut samples from each image in the original road image library; S142, calibrate the samples: mark the samples containing lane lines as positive samples, otherwise mark them as negative samples sample; S143, image classification: export positive samples and negative samples to generate a regional road database. In step S141, at least one small block of m*m rectangular ...
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The present invention relates to the technical field of pattern recognition, and relates to a lane line detection method based on local line segment pattern features, comprising the following steps, S1, establishing an image library; S2, calculating the LLP texture feature vector of the image library; S3, according to the LLP texture feature vector , to iteratively optimize the neural network algorithm. The invention fragments the image into several fragments and then uses the lane line detection algorithm to perform LLP feature recognition to identify the corresponding lane line, effectively avoids noise interference, is beneficial to the detection of straight lines and curves, improves the detection accuracy of lane lines, and solves the problems in the prior art Because the road image is seriously disturbed by shadows, the lanes usually have curves and some roads are bumpy, which leads to high requirements on the image quality of urban roads and low detection accuracy in lane line detection, which can effectively improve the shadow anti-interference performance. The local information is extracted finely and the lack of surrounding remote information is taken into account to achieve the purpose of improving the accuracy of lane line detection.
Description
technical field [0001] The present invention relates to the technical field of pattern recognition, more specifically, to a lane line detection method based on local line segment pattern features. Background technique [0002] Lane line detection is to preprocess the image captured by the camera, and extract the lane marking line or road boundary information from the image. Lane line detection technology is mainly used in the fields of autonomous driving, military safety assisted driving, Internet of Vehicles, intelligent logistics, computer vision, etc. With the rapid development of the automobile industry, while road transportation has brought great convenience to people, the traffic accidents caused by it have become one of the global safety problems. Therefore, intelligent driving systems such as lane departure warning are particularly important. The most important issue of vehicle departure warning is lane line detection and tracking. For more than ten years, domestic ...
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IPC IPC(8): G06K9/00G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06V20/588G06N3/045G06F18/2413
Inventor 林靖宇陈家华陈雪云阳理理
Owner GUANGXI UNIV