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Traffic sign detection and identification method based on collaborative bionic vision in complex city scene

A technology for signs and cities, applied in the intersection of biological information and machine vision technology, can solve the problem of neglecting the correlation of multiple images

Inactive Publication Date: 2018-04-13
CENT SOUTH UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Nowadays, most saliency detection algorithms are aimed at a single image, and often only consider areas with strong contrast or high distinction in a single image, but ignore the correlation between multiple images

Method used

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  • Traffic sign detection and identification method based on collaborative bionic vision in complex city scene
  • Traffic sign detection and identification method based on collaborative bionic vision in complex city scene
  • Traffic sign detection and identification method based on collaborative bionic vision in complex city scene

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Experimental program
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Embodiment 1

[0111] Implementation example 1: Salient region detection based on bionic vision

[0112] Step A: Obtain multiple images to be detected in continuous scenes;

[0113] Step B: extract three bottom-up visual attention cues of the image set to be detected consisting of all the images to be detected, and obtain the clustering collaboration graph of the image set to be detected, such as figure 2 , so that the same or similar areas that recur in the image set can be highlighted;

[0114] Step C: Obtain the attention saliency map of each image to be detected by extracting two bottom-up visual attention cues of each image to be detected;

[0115] Wherein, the acquisition process of the cluster synergy map in the step B is the same as that of the single image attention saliency map in the step C, when obtaining the cluster synergy map, first perform scale normalization processing on each image to be detected ,details as follows:

[0116] Using the bilinear interpolation method, res...

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Abstract

The invention discloses a traffic sign detection and identification method based on collaborative bionic vision in a complex city scene, which includes the following steps: A, obtaining multiple to-be-detected images in a continuous scene; B, obtaining a cluster collaboration map of the to-be-detected image set; C, obtaining an attention saliency map of each to-be-detected image; D, obtaining a collaboration saliency map corresponding to each to-be-detected image; E, locating a sign ROI (Region of Interest); F, carrying out two-level biologically inspired transformation on the sign ROI by using a forward channel; and H, using a feature transformation map and traffic sign template images pre-stored in a database to carry out Pearson correlation calculation to complete the identification ofthe to-be-detected images. In the method, visual processing of a target by the human brain is simulated, and bottom-up visual processing and top-down visual processing processes are integrated. The collaborative nature of global images is considered, so that image location is accurate, and the robustness of identification is strong.

Description

technical field [0001] The invention belongs to the intersecting field of biological information and machine vision technology, and in particular relates to a traffic sign board detection and recognition method for collaborative bionic vision in complex urban scenes. Background technique [0002] Traffic sign recognition is one of the core issues in driver assistance systems and driverless systems. Among them, the detection and recognition of traffic signs in complex urban scenes is a hot and difficult point in the field of computer vision. However, using traditional computer vision algorithms to detect and recognize objects in complex scene images is a very challenging task. As we all know, the human visual system will focus on the outline, color, edge direction and contrast of the target without prior knowledge, so humans can effectively block interference information in complex scenes and quickly lock the target of interest . Inspired by this visual processing mechanis...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/32G06K9/46G06K9/62G06T7/13G06T7/187
CPCG06T7/13G06T7/187G06V20/582G06V10/25G06V10/44G06V10/462G06F18/23
Inventor 余伶俐夏旭梅周开军孔德成严孝鑫邵玄雅
Owner CENT SOUTH UNIV
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