Coating surface defect identification method based on deep learning

A defect recognition and deep learning technology, applied in neural learning methods, character and pattern recognition, image enhancement and other directions, can solve the problems of poor recognition accuracy and speed, and achieve the effect of fast and high-precision classification and recognition operations and fast extraction

Active Publication Date: 2021-06-11
HARBIN ENG UNIV
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Problems solved by technology

[0005] The purpose of the present invention is to propose a coating surface defect recognition method based on deep learning to solve the problem of poor recognition accuracy and speed in the automatic detection process of multi-type coating surface defects. Realize high-precision identification of coating surface defects in a short time, which can provide an effective implementation method for automatic detection of coating surface defects

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  • Coating surface defect identification method based on deep learning
  • Coating surface defect identification method based on deep learning
  • Coating surface defect identification method based on deep learning

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Embodiment Construction

[0034] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0035] The present disclosure is capable of various embodiments, and adaptations and changes are possible therein. It should be understood, however, that there is no intention to limit various embodiments of the present disclosure to the particular embodiments disclosed herein. Rather, the present disclosure should be understood to cover all adaptations, equivalents and / or alternatives falling within the spirit and scope of the various embodiments of the present disclosure.

[0036] Terms used in various embodiments of the present disclosure are for the purpose of describing specific embodiments only and are not intended to limit the various embodiments of the present disclosure. As used herein, singular forms are intended to include plural forms as well, unless the context clearly dictates otherwise. Unless otherwise defined, all t...

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Abstract

The invention provides a coating surface defect identification method based on deep learning. The method comprises the following steps: S1, selecting a feature extraction network; s2, designing an inverted pyramid type classifier; s3, constructing an identification model; s4, training the recognition model by using the optimized and adjusted training method; and S5, identifying the surface defects of the coating. According to the coating surface defect identification method based on deep learning provided by the invention, rapid and high-precision identification of the coating surface defect can be realized under the condition of a small sample, and the method has a relatively good application prospect in the field of automatic detection and identification of the coating surface defect.

Description

technical field [0001] The invention belongs to the field of surface defect recognition and deep learning, in particular to a method for recognizing coating surface defects based on deep learning. Background technique [0002] Coating is a covering layer with a certain thickness formed on the surface of metal or non-metal substrate, which is different from the substrate material and has certain reinforcement, protection or special functions. It is widely used in modern mechanical equipment. However, in the process of actual spraying and use, the coating is prone to various defects, such as sagging, orange peel, exposed bottom, cracking, etc., which greatly reduces the overall protective performance of the coating and shortens the service life. Layers use devices to effect. [0003] In the past, the method of detecting and identifying coating surface defects by human eyes was difficult to meet the precision and speed requirements of industrial production due to high cost, lo...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00G06K9/46G06K9/62G06N3/04G06N3/08
CPCG06T7/0002G06N3/08G06T2207/10004G06T2207/20081G06T2207/20084G06T2207/30168G06V10/44G06N3/045G06F18/214G06F18/241
Inventor 陈宗阳吕永胜赵辉沙建军赵博房海波沙香港
Owner HARBIN ENG UNIV
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