Due to the influence of objective environmental conditions in the image acquisition stage, the image quality of the existing methods is poor, which makes the effect of defect detection not ideal. In this paper, research on product appearance defect detection based on deep learning machine vision is proposed. A product appearance model is constructed by using a Gaussian mixture, and multiple sub-images with different sizes are used as the data support of the multi-scale expression of the Gaussian model to calculate the Gaussian difference features of the image. A Faster R-CNN network algorithm in deep learning is used to recognize defects. The multi-task mechanism is introduced in the setting of the loss function to realize the concurrent function of defect detection. The mean pooling and maximum pooling operations are introduced in the setting of the classification regression network to realize the comprehensive detection of target images. Judge whether that product has a defect according to the output result of the algorithm and the DOG characteristics of the image. The test results show that the accuracy of the designed method for various types of defect detection is more than 90. 00% and it has a high detection efficiency.
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Machine Vision Product Appearance Defect Detection Based on Deep Learning
Semantic Scholar · Computer Science · 2022
Abstract
Due to the influence of objective environmental conditions in the image acquisition stage, the image quality of the existing methods is poor, which makes the effect of defect detection not ideal. In this paper, research on product appearance defect detection based on deep learning machine vision is proposed. A product appearance model is constructed by using a Gaussian mixture, and multiple sub-images with different sizes are used as the data support of the multi-scale expression of the Gaussian model to calculate the Gaussian difference features of the image. A Faster R-CNN network algorithm in deep learning is used to recognize defects. The multi-task mechanism is introduced in the setting of the loss function to realize the concurrent function of defect detection. The mean pooling and maximum pooling operations are introduced in the setting of the classification regression network to realize the comprehensive detection of target images. Judge whether that product has a defect according to the output result of the algorithm and the DOG characteristics of the image. The test results show that the accuracy of the designed method for various types of defect detection is more than 90. 00% and it has a high detection efficiency.