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Paper defect classification method based on multi-scale morphology combined with convolutional neural network

A convolutional neural network and defect classification technology, applied in the field of paper defect classification combined with multi-scale morphology and convolutional neural network, can solve the problems of simple texture, single background, single underlying feature, etc., achieving high accuracy and low time consumption Effect

Inactive Publication Date: 2018-08-21
SHAANXI UNIV OF SCI & TECH
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Problems solved by technology

The texture of the paper defect image is simple, the background is single, and the paper defect image such as black spots, holes, pad marks, and wrinkles are small and a small number of defects
Traditional methods can only extract a single underlying feature, resulting in unsatisfactory classification results

Method used

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  • Paper defect classification method based on multi-scale morphology combined with convolutional neural network
  • Paper defect classification method based on multi-scale morphology combined with convolutional neural network
  • Paper defect classification method based on multi-scale morphology combined with convolutional neural network

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Embodiment 1

[0053] In order to test the effectiveness and superiority of the present invention for classifying paper defect images, the simulation experiments are carried out under the hardware environment of CPU: Intel(TM) i7-6700U, 3.3GHz, memory 16GB, NVIDIA Quadro K620 graphics card and software environment of MATLABR2017a of.

[0054] Three comparison methods are used: the traditional method extracts the HOG feature of paper defects for classification, and obtains the classification result (HOG+SVM), and extracts the LBP feature for classification to obtain the classification result (LBP+SVM). The classification results obtained by inputting the image to the CNN model after image enhancement (Canny+CNN), the classification results obtained by the Sobel operator for four types of paper defects after image enhancement and input to the CNN model (Sobel+CNN), and the Prewitt operator for the four types of paper defects The classification result (Prewitt+CNN) obtained by inputting the ima...

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Abstract

The invention relates to a paper defect classification method based on multi-scale morphology combined with a convolutional neural network. Morphological operation is performed on original images by using structural elements different in scale; multi-scale morphological gradient images are obtained through weighted fusion of gradient images under different scales. In order to increase paper defectcontrast and highlight defect gradient features and information features of defect edges, the multi-scale morphological gradient images and the original defect images are subjected to weighted fusionto achieve defect image enhancement; the images are input to convolutional neural network for feature extraction and classification, and the convolutional neural network is used in classification ofpaper defects so that accurate classification of the paper defects can be rapidly achieved. The method has the advantages of being simple, short in time consumption and high in recognition precision.

Description

technical field [0001] The invention belongs to the technical field of image processing and pattern recognition, and particularly relates to a paper defect classification method combining multi-scale morphology and convolutional neural network. Background technique [0002] Feature extraction is a key step in pattern recognition and has important applications in image analysis and pattern recognition. The traditional feature extraction method of image classification is to define a feature in advance, and then perform feature extraction and classification according to the defined feature. In practical applications, paper images are easily affected by factors such as light and environment, making defect detection, feature extraction and classification a hot spot in the paper industry. At present, scholars have proposed a variety of paper defect classification algorithms. Yuan Hao et al. proposed to apply the support vector machine to the actual paper defect classification by...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06K9/44
CPCG06V10/34G06F18/241
Inventor 雷涛张宇啸薛丁华加小红
Owner SHAANXI UNIV OF SCI & TECH
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