Method of detecting fruit surface defect based on low pass filter

Abstract

The invention discloses a method of detecting fruit surface defects based on low pass filter. Binary mask image B' is constructed based on B component of fruit colorful image. R component f(x, y) of removed background is obtained by the dot product of B' and R component. Central transformation of f(x, y) is performed to obtain f'(x, y) and discrete Fourier transformation of f'(x, y) is performed to obtain F(u, v). Low frequency component G(u, v) is obtained by the multiplication of F(u, v) and low pass filter H(u, v). g(x, y) is obtained by negative discrete Fourier transformation of G(u, v). i'(x, y), which is the surface brightness image of f(x, y), is obtained by the multiplication of g(x, y) and the function of (-1)x + y. Homogenization brightness image f''(x, y) is obtained by the division of f(x, y) and i'(x, y). Fruit surface defects are detected by single threshold method of f''(x, y). Fruit surface defects are easily detected in the invention by single threshold method, wherein homogenization correction of fruit surface brightness is carried out. The method of the invention overcomes the complexity of conventional defect inspection algorithm and is free of considering the sizes and shapes of fruits to be detected, thus is better adjusted than standard ball brightness adjustment method. The images are acquired by a single camera, thereby avoiding the problem of low processing speed due to the information fusion of images from a plurality of cameras.

Description

technical field [0001] The invention relates to a method for detecting fruit surface defects, in particular to a method for detecting fruit surface defects based on low-pass filtering. Background technique [0002] Fruit surface defects are one of the most powerful factors determining fruit prices, because external defects are the most direct reflection of fruit quality. The rapid identification of defects has always been the most difficult and most interesting research content in fruit real-time grading, but the ideal research results have not been achieved. One of the main difficulties in using machine vision technology to detect fruit surface defects is that because the fruit is usually in the shape of a sphere or ellipsoid, at the edge of the fruit, the angle between the reflection direction of the light and the camera is very large. According to Lambert’s light reflection According to the law, from the camera direction, the brightness of the fruit edge is low, which me...

AI Technical Summary

Problems solved by technology

The main problem of multi-camera fusion is: after the number of cameras is increased, the workload of image processing will be greatly increased, which will seriously reduce the processing speed of the machine vision system.

Images