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System for detecting surface defects of metal plate based on deep convolution neural network

A technology of metal strip and deep convolution, which is applied in the direction of biological neural network model, neural architecture, optical test defects/defects, etc., can solve the problems that cannot meet the accuracy and real-time requirements of industrial on-line detection, and improve detection speed effect

Inactive Publication Date: 2017-11-07
UNIV OF SCI & TECH BEIJING
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Problems solved by technology

[0004] The technical problem to be solved by the present invention is to provide a metal strip surface defect detection system based on a deep convolutional neural network to solve the problems existing in the prior art that cannot meet the accuracy and real-time requirements of on-line detection in the industry

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  • System for detecting surface defects of metal plate based on deep convolution neural network
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  • System for detecting surface defects of metal plate based on deep convolution neural network

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Embodiment Construction

[0026] In order to make the technical problems, technical solutions and advantages to be solved by the present invention clearer, the following will describe in detail with reference to the drawings and specific embodiments.

[0027] Aiming at the existing problem that the real-time requirements of on-line detection in the industry cannot be met, the present invention provides a metal strip surface defect detection system based on a deep convolutional neural network.

[0028] Such as figure 1 As shown, the metal strip surface defect detection system based on the deep convolutional neural network provided by the embodiment of the present invention includes: a camera 11, a mounting frame 12 for installing the camera 11, and a processor module 13; wherein,

[0029] The mounting frame 12 straddles the industrial site conveyor belt;

[0030] The camera 11 is installed on the mounting frame 12 for real-time collection of surface images of metal strips on the conveyor belt;

[0031...

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Abstract

The invention provides a system for detecting surface defects of a metal plate based on a deep convolution neural network. The system can be used for detecting the surface defects of a metal plate in real time. The system comprises a camera, a mounting frame used for mounting the camera and a processor module, wherein the mounting frame stretches across a conveyer belt on an industrial site; the camera is arranged on the mounting frame and is used for collecting a surface image carried by the metal plate on the conveyer belt in real time; the processor module is used for adopting a deep convolution neural network algorithm for recognizing the defects of the surface image carried by the metal plate collected in real time. The system provided by the invention is suitable for the technical field of machine vision.

Description

technical field [0001] The invention relates to the technical field of machine vision, in particular to a detection system for surface defects of metal strips based on deep convolutional neural networks. Background technique [0002] Metal sheet and strip are indispensable raw materials in industries such as automotive, machine building, chemical, aerospace and shipbuilding. The surface defect of the metal strip refers to the inhomogeneous physical or chemical properties of the local area of ​​the metal strip surface due to the process or other various reasons during the production and processing of the metal strip. Common surface defects of metal strips include roll marks, stains, scratches, holes, leaks, depressions, bubbles, foreign matter, and peeling. Surface defects are parts with high atomic activity, and often become the origin of metal corrosion. The existence of surface defects will greatly reduce the fatigue resistance of parts, damage the quality of parts surfac...

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Application Information

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IPC IPC(8): G01N21/89G06N3/04
CPCG01N21/8914G01N2021/8918G01N2021/8887G06N3/045
Inventor 李江昀任起锐郑俊锋
Owner UNIV OF SCI & TECH BEIJING
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