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Medical large transfusion machine vision on-line detection method

A technology of machine vision and detection method, applied in the direction of instruments, simulators, computer control, etc., can solve the problems of low precision, low efficiency of manual detection, high rate of missed detection, etc.

Inactive Publication Date: 2008-04-23
HUNAN UNIV
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  • Application Information

AI Technical Summary

Problems solved by technology

[0003] The problem to be solved by the present invention is: aiming at the technical problems existing in the prior art, the present invention provides a method that can overcome the problems of low manual detection efficiency, slow speed, low precision, high missed detection rate, and easy fatigue of detection personnel, thereby improving Medical large infusion machine vision online detection method for the degree of automation and product quality of pharmaceutical large infusion

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  • Medical large transfusion machine vision on-line detection method
  • Medical large transfusion machine vision on-line detection method
  • Medical large transfusion machine vision on-line detection method

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Embodiment Construction

[0061] The present invention will be described in further detail below in conjunction with the accompanying drawings and specific embodiments.

[0062] As shown in Figure 1, the specific process of the machine vision online detection method for large medical infusion of the present invention is:

[0063] 1. Acquire continuous images. This step is mainly to complete the acquisition of continuous images, and use the pre-completed camera calibration to obtain the actual distance represented by the distance between adjacent pixels of the captured image, and provide data support for calculating the maximum diameter of particles during the image judgment process. The large infusion bottle stops immediately after rotating at high speed on the circular track, and the camera continuously collects the infusion images after the emergency stop to form a sequence of images, which are sent to the industrial computer to determine whether there are visible foreign objects. Among them, the ci...

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Abstract

The method comprises: 1) getting the sequence image after the massive transfusion rotation stops in crash; 2) making preprocess for the sequence image; 3) completing the extraction of motion particle in the sequence image; 4) selecting the multi-step threshold method to gradually determining the optimal partitioning threshold of the micro foreign substance grey scale image to realize the partitioning for the motion target; 5) using the eight connected domain mark to mark the connected area of foreign substance, and calculating the internal maximal diameter of same connected area; 6) making calculation for the target maximal diameter and eccentricity to estimate the quality state of the transfusion and to make classification for the type of the foreign substance; finally, sending out the control signals.

Description

technical field [0001] The invention mainly relates to the detection field of large medical infusions, in particular to a machine vision online detection method for large medical infusions. Background technique [0002] In the prior art, large-scale medical infusions refer to large-capacity injections of more than 100 milliliters. Today, there are more than 300 manufacturers of large-scale infusions in my country, with a production capacity of 6.9 billion bottles per year and an annual output value of more than 10 billion yuan. Among these Among the products, more than 90% of the products are packaged in glass bottles. Although new types of packaging continue to emerge, more than 46% of developed cities such as Shanghai use flexible packaging infusions, and the output of plastic packaging infusions accounts for more than 5% of the market share. It is estimated that after several years, glass bottles will still be the main infusion solution products in China. These products pa...

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Application Information

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IPC IPC(8): G06T7/00G06K9/34G06K9/46G05B19/048
Inventor 王耀南鲁娟周博文张辉余洪山秦虹朱惠峰
Owner HUNAN UNIV
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