Defect detection and classification method based on FCNs (fully convolutional networks) and applied to galvanized stamping parts

A convolutional neural network, defect detection technology, applied in the field of image processing and deep learning, can solve the problems of complex image processing process, difficult to meet engineering requirements, unstable image quality, etc., achieve high classification accuracy, good real-time performance, avoid Effects of preprocessing and feature extraction

Inactive Publication Date: 2018-01-19
TIANJIN POLYTECHNIC UNIV
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Problems solved by technology

However, for the defect detection of galvanized stamping parts, due to the complex image processing process, difficult featu

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  • Defect detection and classification method based on FCNs (fully convolutional networks) and applied to galvanized stamping parts
  • Defect detection and classification method based on FCNs (fully convolutional networks) and applied to galvanized stamping parts
  • Defect detection and classification method based on FCNs (fully convolutional networks) and applied to galvanized stamping parts

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[0029] Process flow of the present invention such as figure 1 As shown, first, collect samples containing various types of defects, calculate the standard deviation of the gray level of the samples, and distinguish the defective workpieces from the qualified workpieces according to the preliminary binary classification of the standard deviation; then, simple preprocessing of the initially screened samples improves Contrast, make the features more obvious, and extract the region of interest; use the processed samples as the input of the fully convolutional neural network for training; finally set the threshold for the output samples to judge the type of defects. The specific implementation process of the technical solution of the present invention will be described below in conjunction with the accompanying drawings.

[0030] 1. Collect defective samples;

[0031] Acquire sample images containing various types of blemishes.

[0032] 2. Preliminary two classifications based on...

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Abstract

The invention relates to a defect detection and classification method based on FCNs (fully convolutional networks) and applied to galvanized stamping parts. The method comprises steps as follows: collecting samples with various types of defects; performing preliminary binary classification according to image gray standard deviation, and distinguishing qualified workpieces and defective workpieces;preprocessing the preliminarily screened samples to improve contrast, and extracting region of interests to serve as improved FCN inputs for training; calculating pixel values of output workpiece images, and setting a threshold value to judge the types of the workpiece defects and performing classification. Defect detection and classification are performed through combination of image processingand the FCNs, so that complicated preprocessing and feature extraction are avoided, and the defects of the galvanized stamping parts can be better detected and classified.

Description

technical field [0001] The invention relates to a defect detection and classification method for galvanized stamping parts based on a fully convolutional neural network. The method combines image processing and a fully convolutional neural network, belongs to the technical field of image processing and deep learning, and can detect galvanized stamping parts. surface defects and classify them. Background technique [0002] Stamping parts are an important accessory that is widely used in various industrial fields, especially in the field of automobile production, and has attracted more and more attention because of its wide application. In the production process, if the defective products cannot be detected and eliminated in time, it will seriously affect the subsequent assembly process and lead to a decline in the overall quality of the product. Therefore, its quality monitoring helps to optimize the entire production chain, thereby improving the quality of the finished produ...

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Application Information

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IPC IPC(8): G01N21/95B07C5/342
Inventor 耿磊肖志涛王曼迪冷彦奕吴骏张芳
Owner TIANJIN POLYTECHNIC UNIV
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