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A Deep Learning-Based Coating Surface Defect Recognition Method

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: 2022-07-29
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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  • A Deep Learning-Based Coating Surface Defect Recognition Method
  • A Deep Learning-Based Coating Surface Defect Recognition Method
  • A Deep Learning-Based Coating Surface Defect Recognition Method

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Embodiment Construction

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

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

[0036] The terminology used in the various embodiments of the present disclosure is for the purpose of describing particular embodiments only and is not intended to limit the various embodiments of the present disclosure. As used herein, the singular is intended to include the plural as well, unless the context clearly dictates otherwise. Unless otherwise def...

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Abstract

The present invention provides a method for identifying coating surface defects based on deep learning, comprising the following steps: S1: selecting a feature extraction network; S2: designing an inverted pyramid classifier; S3: constructing a recognition model; S4: using training after optimization and adjustment The method trains the recognition model; S5: recognizes the surface defects of the coating. The invention provides a coating surface defect identification method based on deep learning, which can realize fast and high-precision identification of coating surface defects in the case of small samples, and has a good application prospect in the field of automatic detection and identification of coating surface defects .

Description

technical field [0001] The invention belongs to the fields of surface defect identification and deep learning, in particular to a coating surface defect identification method based on deep learning. Background technique [0002] Coating is a layer formed on the surface of a metal or non-metal substrate with a certain thickness, which is different from the substrate material and has a certain strengthening, protection or special function. It has been widely used in modern machinery and equipment. However, in the actual spraying and use process, the coating is prone to various defects, such as sagging, orange peel, exposed bottom, cracks, etc., which greatly reduces the overall protective performance of the coating, shortens the service life, and further affects the coating. Layer using the device has an impact. [0003] The previous methods of detecting and identifying coating surface defects relying on human eyes are difficult to meet the precision and speed requirements of...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T7/00G06V10/44G06V10/764G06V10/774G06V10/82G06K9/62G06N3/04G06N3/08
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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