Welding visual detection method and device based on convolutional neural network

A convolutional neural network and visual detection technology, applied in the field of crawlers, can solve problems such as the difficulty of explicit feature extraction and the inability to guarantee accuracy

Active Publication Date: 2016-08-24
ZHEJIANG UNIV OF TECH
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Problems solved by technology

The main problem of this invention is that the judgment of welding effect is to compare the picture information of solder joints during and after welding with the standard formula data of solder joints d

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  • Welding visual detection method and device based on convolutional neural network
  • Welding visual detection method and device based on convolutional neural network
  • Welding visual detection method and device based on convolutional neural network

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[0074] The present invention will be further described below in conjunction with the drawings.

[0075] Reference Figure 1 ~ Figure 7 , A welding visual inspection method based on convolutional neural network, including the following processes:

[0076] In the training phase, it is first necessary to collect training samples. In this embodiment, a total of 10,000 training samples are collected. Among these 10,000 samples, 500 are welding defect pictures, and the remaining 9,500 are the aforementioned 500 welding defect pictures. Adding Gaussian white noise, picture rotation, color transformation, translation, contrast stretching, flipping and other image processing methods to obtain welding defect pictures. Then input these samples into the convolutional neural network for training, and obtain the connection weight and bias value of the convolutional neural network.

[0077] In this embodiment, the structure of the convolutional neural network adopts image 3 In the structure show...

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Abstract

A welding visual detection method based on a convolutional neural network includes the following steps that firstly, at the training stage, a training sample is input into the convolutional neural network, and the connection weight and offset value of the convolutional neural network are obtained; secondly, at the testing state, a welding picture is read in and preprocessed with the digital picture processing technology, and a region of interest is extracted and then subjected to picture size normalization processing to serve as input of the convolutional neural network. The invention further provides a welding visual detection device based on the convolutional neural network. The welding visual detection device based on the convolutional neural network comprises a crawling mechanism, a power transmission mechanism, visual detection equipment and a weld defect detection and analysis system. By means of the welding visual detection method and device based on the convolutional neural network, the automaton and intelligence level is improved, the detection precision is effectively improved, and the detection speed is effectively increased.

Description

technical field [0001] The invention belongs to the application of crawler, omnidirectional visual sensor, wireless communication, deep learning and computer vision technology in weld defect recognition and detection, and relates to a welding visual detection method and device. Background technique [0002] In the manufacturing industry, welding is the basis of many manufacturing methods, and its quality determines the reliability of the product. Welding quality inspection is an important means to ensure welding quality. At present, the link quality inspection methods mainly include: destructive inspection and non-destructive inspection. Compared with non-destructive testing, destructive testing has higher reliability, but the workpiece must be destroyed. As the most widely used non-destructive inspection method, visual inspection method can realize the quality analysis of weld surface topography, surface geometry and discontinuities or defects existing on the surface. [...

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Application Information

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IPC IPC(8): G01N21/88
CPCG01N21/8851G01N2021/8854G01N2021/8883G01N2021/8887G01N2201/1296
Inventor 胡克钢汤一平吴挺鲁少辉韩国栋陈麒袁公萍
Owner ZHEJIANG UNIV OF TECH
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