Target detection algorithm based on targeted potential areas analysis and application thereof

Abstract

The invention discloses a target detection algorithm based on targeted potential areas analysis, comprising the following steps: acquiring image data through a camera, carrying out super pixel segmentation on the image, quickly extracting an edge map of a super pixel segmentation map, acquiring the size of super pixels and a bounding rectangle thereof, combining super pixel blocks obtained through segmentation to get an initial area set, and taking the bounding rectangles of the areas as an initial rectangle set; calculating the tightness score of a super pixel set in a local rectangle area, the contour score of an optimal contour and the compactness score of a salient super pixel set in the contour; fusing the tightness score, the contour score and the compactness score through a data driving approach to get a targeted score of a final evaluation area; and sorting the initial rectangle set in a descending order based on the score, and selecting a high-probability area for target detection. The algorithm of the invention can be used in robot vision navigation and automobile aided-driving.

Description

technical field [0001] The invention relates to machine vision technology, in particular to a target detection algorithm based on target potential area analysis and its application. Background technique [0002] Object detection is one of the important technologies in machine vision, widely used in object recognition, object tracking and scene analysis and other fields. However, traditional target detection methods use complex features combined with multi-scale pyramid search, and the algorithm efficiency is relatively low. In order to improve the efficiency of the target detection algorithm, the target latent region extraction method is introduced as a preprocessing algorithm for target detection to replace the traditional search method. This method quickly extracts a small number of target potential areas and sends them to the target detection algorithm in a simple and efficient way, greatly reducing the number of areas that need to be analyzed by the target detection alg...

AI Technical Summary

Problems solved by technology

[0007] The above methods usually only consider a certain feature to evaluate the target of the region, and often cannot effectively evaluate the target score, resulting in the inability to effectively recommend a small number of regions containing the target in the final region sorting

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