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Rubber glove detection method and system based on transfer learning

A rubber glove and transfer learning technology, applied in neural learning methods, image data processing, image enhancement, etc., can solve the problem of high misjudgment rate, achieve the effect of improving detection speed, improving accuracy and efficiency, and shortening training time

Active Publication Date: 2020-08-28
SHANGHAI UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the misjudgment rate is higher in cases where the color of the oil stain is light, the oil stain area is close to the background, and the oil stain area is small.

Method used

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  • Rubber glove detection method and system based on transfer learning
  • Rubber glove detection method and system based on transfer learning
  • Rubber glove detection method and system based on transfer learning

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Embodiment Construction

[0044] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0045]The object of the present invention is to provide a method and system for detecting rubber gloves based on transfer learning, so as to improve the accuracy and efficiency of rubber glove detection.

[0046] In order to make the above objects, features and advantages of the present invention more comprehensible, the present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodimen...

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Abstract

The invention relates to a rubber glove detection method and system based on transfer learning. The method comprises the following steps: acquiring a rubber glove image set; performing image expansionon the rubber glove image training set, and determining the rubber glove image training set after data enhancement; obtaining a VGG16 model; training the VGG16 model by adopting a transfer learning method according to the rubber glove image training set after data enhancement, and determining the trained VGG16 model; acquiring a to-be-detected rubber glove image; and determining a detection result of the to-be-detected rubber glove image according to the to-be-detected rubber glove image and the trained VGG16 model. According to the rubber glove detection method and system based on transfer learning provided by the invention, the accuracy and efficiency of rubber glove detection are improved.

Description

technical field [0001] The invention relates to the field of oil stain defect detection of rubber gloves, in particular to a method and system for detecting rubber gloves based on transfer learning. Background technique [0002] In recent years, driven by healthcare, food hygiene, and some emerging industries, the demand for rubber gloves is increasing. According to statistics, the global rubber glove market has reached 228.2 billion pieces in 2017, and it is predicted to grow at a growth rate of 8%-10% year by year. Under such a huge market demand, the quality of products has become the top priority, and quality inspection has become an indispensable key link in the production process of rubber gloves. The traditional judgment method for rubber glove defects is divided into two parts: first, before the glove leaves the mold, the staff relies on the naked eye to check whether there are defective products; second, before packaging, some products are selected by batch for ins...

Claims

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Application Information

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IPC IPC(8): G06T7/00G06N3/04G06N3/08
CPCG06T7/0004G06N3/08G06T2207/20081G06T2207/20084G06T2207/30108G06N3/047G06N3/045Y02P90/30
Inventor 黄宝成陆小锋孙广玲秦臻李忠阳叶新瑞钟宝燕
Owner SHANGHAI UNIV