Machine vision biological enzyme liquid defect detection method realized through AI technology
A technology of machine vision and defect detection, applied in biological neural network models, optical test defects/defects, instruments, etc., can solve problems such as high misjudgment rate, affect output, increase the risk of missed inspection, etc., to improve production efficiency, The effect of improving precision
Pending Publication Date: 2022-07-12
无锡埃姆维工业控制设备有限公司
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Problems solved by technology
[0004] The purpose of the present invention is to provide a machine vision biological enzyme liquid defect detection method realized by AI technology to solve the problem that the color of the product itself changes, which will lead to a high misjudgment rate and affect the output. If the threshold is relaxed, it will cause Technical issues that increase the risk of missed detection
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[0018] combine figure 1 Shown: a machine vision biological enzyme liquid defect detection method realized by AI technology, including the following steps:
[0019] The target product arrives at the detection position through the material belt; PLC triggers the camera to take a photo; does photo inspection, and will send the inspection results to other upper computers; the upper computer displays the inspection results;
[0020] The photographing detection includes doing the current photographing detection or reaching the next photographing position to shoot, and reaching the next photographing position to shoot is to send a photographing completion instruction by vision, and the mechanism moves to the next position.
[0021] combine figure 2 , 3 As shown, the above method steps are specifically expanded: the enzyme liquid plate will run on the material belt, when the product reaches the position, the PLC will transmit the signal to the software through the IO communication...
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The invention belongs to the field of biological detection, and particularly relates to a machine vision biological enzyme liquid defect detection method realized through an AI technology, which comprises the following steps: a target product reaches a detection position through a material belt; the PLC triggers the camera to photograph; photographing detection is carried out, and the detection result is sent to other upper computers; the upper computer displays a detection result; wherein the photographing detection comprises the step of performing the photographing detection or the step of reaching the next photographing position for photographing, the step of reaching the next photographing position for photographing is to send a photographing completion instruction through vision, and the mechanism moves to the next position. According to the invention, the detection accuracy is improved, the qualified rate is increased to 99% or above from 95% in the prior art, the production efficiency is greatly improved, and excess benefits are created for customers.
Description
technical field [0001] The invention belongs to the field of biological detection, and in particular relates to a machine vision biological enzyme liquid defect detection method realized by AI technology. Background technique [0002] The traditional manufacturing industry is constantly transforming and developing with the dividends of emerging digital technologies such as "cloud computing", "Internet of Things", "big data" and "artificial intelligence". In the process of transformation from traditional manufacturing to intelligent manufacturing, enterprises have a large and urgent need for product quality control and information traceability. Deep learning visual inspection technology has therefore developed into an important branch of machine vision. It is widely used in many fields. [0003] Defects in the enzyme solution will directly affect the accuracy of the doctor's testing of the relevant patient's physical index parameters, resulting in unreasonable diagnosis deci...
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IPC IPC(8): G06T7/00G06K9/62G06N3/02G01N21/89G01N21/88G06V10/82G06V10/774
CPCG06T7/0012G06N3/02G01N21/8806G01N21/8851G01N21/89G01N2021/8887G01N2021/8883G01N2021/888G06T2207/30004G06T2207/20081G06T2207/20084G06F18/214
Inventor 杨得坡王振宇
Owner 无锡埃姆维工业控制设备有限公司