Self-learning chemical fiber spindle intelligent appearance detection system and method based on big data

An appearance inspection and self-learning technology, which is applied in the fields of optical testing defects/defects, measuring devices, scientific instruments, etc., can solve problems such as affecting the shooting effect, waste of manpower, material resources, financial resources, and inefficient and accurate detection of appearance defects of silk ingots. To achieve the effect of reducing workload and improving work efficiency

Active Publication Date: 2017-06-13
杭州慧芯智识科技有限公司
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  • Claims
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Problems solved by technology

[0006] The present invention mainly solves the waste of manpower, material resources and financial resources in the prior art when sorting the silk ingots with appearance defects manually, the lack of position and light source control of the automatic detection system affects the shooting effect, and then affects the subsequent detectio

Method used

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  • Self-learning chemical fiber spindle intelligent appearance detection system and method based on big data
  • Self-learning chemical fiber spindle intelligent appearance detection system and method based on big data
  • Self-learning chemical fiber spindle intelligent appearance detection system and method based on big data

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Embodiment

[0097] In this embodiment, an intelligent appearance detection system for chemical fiber ingots based on big data self-learning, such as figure 1 As shown, it includes a tray 1 for loading silk ingots, and a transfer unit 2 for transporting the trays. A dark box 3 is arranged on the conveying unit, and the dark box is provided with an entrance and an exit. The image acquisition unit 5 for image acquisition of the bottom surface and the bottom surface respectively, the image acquisition unit sends the acquisition information to the image processing unit 6 of the system for defect analysis, and the positioning unit for locating the shooting position of the silk ingot. Among them, the sorting unit receives the defect analysis results and sorts the output silk ingots; the image processing unit adopts the differential method to locate the wire detection area on the collected images, and performs oil pollution detection on the detection area.

[0098] like figure 1 and image 3 A...

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Abstract

The invention relates to an automatic classifying type spindle appearance detection system and method, which solve the problems of waste of labor, material and cost, lack of position and light source control, influence to subsequent appearance detection result, and low efficiency and accuracy on appearance defect detection of the spindle during sorting of spindles with appearance defects in a manual manner. The system comprises a tray and a conveying unit, wherein a dark box is arranged on the conveying unit; a sorting unit is arranged on the conveying unit behind the dark box; an image collection unit is arranged in the dark box; the appearance defect of the spindle is analyzed by collecting an image of each surface of the spindle, and the spindle is automatically classified according to the analysis result. Compared with the manual manner, the system has the advantages that a large amount of labor, material and cost is reduced; compared with common check system, the function of automatically classifying the defective spindles is added, the workload is reduced, and the working efficiency is improved; by providing a self-learning function, the accuracy of oil dirt detection is further improved.

Description

technical field [0001] The invention relates to a wire ingot appearance defect detection technology, in particular to an automatic classification type wire ingot appearance detection system and method. Background technique [0002] Defects on the chemical fiber spindles not only affect the appearance of the spindles, but also affect the grade of the spindles, and even broken filaments may occur, affecting the production of downstream links. At present, the silk ingots with appearance defects are generally classified manually, but using manual methods is undoubtedly a huge waste of manpower, material resources and financial resources. If you want to use highly automated detection methods, you need to use sophisticated control systems, efficient detection algorithms and accurate classification techniques. [0003] The existing silk ingot detection methods only detect a single variety of silk ingots under specific circumstances, and the detection results cannot be automaticall...

Claims

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Application Information

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IPC IPC(8): G01N21/88G01N21/94B07C5/36
CPCB07C5/362G01N21/8806G01N21/8851G01N21/94G01N2021/8822G01N2021/8835G01N2021/8841G01N2021/8854G01N2021/8887G01N2021/945
Inventor 周奕弘汪太平
Owner 杭州慧芯智识科技有限公司
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