Ellipsoidal fruit dimension rapid detection method based on characteristic vector orientation

Abstract

The invention discloses an ellipsoidal fruit dimension rapid detection method based on characteristic vector orientation. A fruit edge image is obtained through performing such operation as threshold segmentation, filtering, edge extraction and the like on an obtained fruit image. A rectangular coordinate system is established for the edge image; a covariance matrix of edge coordinates is solved; a characteristic value of the covariance matrix and a unit characteristic vector are solved accordingly; and by use of the unit characteristic vector, a fruit is oriented to enable the longitudinal or transverse diameter direction of the fruit to be parallel to the horizontal axis of the rectangular coordinate system, and then dimension detection is finished by calculating upper, lower, left and right extreme points of the boundary of the fruit. According to the invention, the longitudinal diameter and the transverse diameter of the fruit are rapidly oriented through solving the characteristic vector for boundary coordinate information of the fruit image, enormous operation brought by rotating the fruit image multiple times by use of an MER method is avoided, and the detection speed is improved while the detection precision is guaranteed. Therefore, the method provided by the invention is applied to the real-time detection need of the dimension of the fruit in an ellipsoidal fruit commercial processing process.

Description

technical field [0001] The invention relates to a method for detecting fruit size, in particular to a method for quickly detecting the size of an ellipsoidal fruit based on feature vector orientation. Background technique [0002] The size and shape of fruit is an important part of fruit size detection. As one of the important bases for fruit grading, it is strictly regulated in the fruit grading standards of various countries. Being able to quickly and accurately detect fruit size will be of great help to improve the efficiency of fruit quality detection and fruit grading. [0003] Traditionally, the detection of fruit size generally uses the minimum enclosing rectangle MER (MinimumEnclosingRectangle) method to detect the vertical and horizontal diameters of fruits. This method is based on rotating the fruit, finds the outermost point of the fruit at each angle to obtain its circumscribing rectangle, compares the area or perimeter of the circumscribing rectangle at each a...

AI Technical Summary

Problems solved by technology

But this method is limited by fruit shape (Feng Bin, Wang Chouhua. Fruit size detection method based on computer vision. Journal of Agricultural Machinery, 2003(l):73-75)

[0004] For the size detection of ellipsoidal fruits, the MER method has limitations, and there is a contradiction between the rotation angle interval and the detection accuracy. If the detection accuracy is improved, the rotation angle interval should be reduced, and the calculation time will increase significantly, which is not conducive to real-time detection in production.

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