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

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...

AI Technical Summary

Problems solved by technology

However, for the defect detection of galvanized stamping parts, due to the complex image processing process, difficult feature extraction and unstable image quality, it is difficult to meet the engineering requirements only by using machine vision.

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