Impurity detecting method in medicine detection robot based on time domain features of sequence images

Abstract

The invention discloses an impurity detecting method in a medicine detection robot based on time domain features of sequence images. According to the time domain features of sequence image pixels, bright background region features and dark background region features are mapped to the same feature space, the difference between a bright background and a dark background is eliminated, targets and the backgrounds are classified through the neural network, the targets and the backgrounds can be divided, and impurities are detected according to the impurity feature moving track. The impurity detecting method effectively solves the problem that the division of the bright background images and the dark background images cannot be achieved with a sequence frame difference method, and the impurity detection omission ratio and the false drop rate are lower. Compared with an exiting sequence difference method, the impurity detecting method has the advantages that the speed is higher and is not affected by the number of the sequence frames, and the effect is more obvious especially in the process of processing the sequence images with twenty or more frames; the BP neural network is adopted, the classification standard is automatically established with the sample learning method, the adaptability of the complex background is enhanced, and more sample features can be effectively extracted.

Description

technical field [0001] The invention relates to an impurity detection method based on time-domain features of sequence images in a medical detection robot. Background technique [0002] The medical vision inspection robot is mainly a device for detecting foreign matter in ampoules, large infusions, oral liquids and other solutions. Among them, image processing is the most important component of the detection robot. At present, in common foreign matter detection algorithms, the foreign matter area is separated from the background area. The main process is sequential image difference, superposition, and superimposed image binarization processing, but this method has many shortcomings, which are specifically reflected in the following two aspects: [0003] (1) The background of the image is very complex, and the brightness of the background varies greatly, including dark background and bright background. After differential processing of sequence images, the difference value of ...

AI Technical Summary

Problems solved by technology

[0003] (1) The background of the image is very complex, and the brightness of the background changes greatly, including dark background and bright background. The background is similar to the features of foreign objects, and the segmentation effect is not good;

[0004] (2) In the process of sequential image acquisition, the normalization method is often used to eliminate the influence of uneven background brightness. However, foreign objects appearing in the bright background will be wrongly segmented into the bright background, resulting in missed detection.

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