Bottleneck defect detection method based on gradient direction histograms

Abstract

The invention discloses a bottleneck defect detection method based on gradient direction histograms and belongs to the technical field of machine vision and image processing. The method comprises steps of: stretching a bottleneck annular part into a rectangle, then performing cutting and splicing, using the modified rectangle as a sample image, subjecting the sample image to Gamma correction to normalize the sample image, dividing the sample image into a plurality of windows, calculating the gradient direction histograms in the windows to obtain a feature vector, forming a classifier by using a support vector machine; then, for a bottleneck image to be detected, obtaining the feature vectors of respective detection windows by the gradient direction histograms, and determining whether a current bottleneck is a defect bottleneck in combination with the preformed classifier. The method, on one hand, extracts pixel gradient information in the bottleneck image based on the principle of the gradient direction histograms so as to realize the real-time detection of the bottleneck defect, and on the other hand, improves the action scope of the defective pixels by an interpolation and normalization method so as to achieve the accurate positioning of the bottleneck defect.

Description

technical field [0001] The present invention relates to the technical field of machine vision and video image processing, and more specifically, relates to a method for detecting bottleneck defects based on gradient direction histograms. Background technique [0002] The detection of bottle mouth defects in the traditional industrial inspection field is mostly based on manual naked eye detection. However, human eyes are prone to fatigue, which leads to missed detection and false detection, and the labor cost is high, the efficiency is low, and the reliability is poor. This is very different from large-scale integrated industrial production. Uncoordinated, using computer vision and image processing algorithms to automatically detect bottleneck defects can effectively solve this problem. [0003] At present, edge detection-based bottleneck detection methods, such as Canny edge detection combined with thresholding, are easy to misjudge edge burrs as defects or mistake small def...

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Problems solved by technology

[0007] The problem to be solved by the present invention is: the existing bottle mouth defect detection relies on manual observation, which is inefficient and has a high false detection rate; Meet the real-time requirements of industrial production; the method that can quickly detect bottle mouth defects can only deal with high-quality bottle mouth images, and the effect on complex texture bottle mouth images is poor

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