CN113298772A - Nose wing blackhead image detection method based on deep learning and adaptive threshold method - Google Patents

Nose wing blackhead image detection method based on deep learning and adaptive threshold method Download PDF

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CN113298772A
CN113298772A CN202110552883.7A CN202110552883A CN113298772A CN 113298772 A CN113298772 A CN 113298772A CN 202110552883 A CN202110552883 A CN 202110552883A CN 113298772 A CN113298772 A CN 113298772A
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熊国虹
董璐
孙佳
王远大
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Abstract

The invention discloses a method for detecting a blackhead image of a nasal wing based on deep learning and an adaptive threshold method, which comprises the following steps: s1, collecting a facial feature segmentation data set, training a CNN cascade network and storing network parameters; s2, calling the trained CNN cascade network to perform facial feature segmentation, and extracting a nose image in the face image; s3, extracting a blue single channel from the nose image, removing the influence of edges, light rays and shadows, and removing the nostril part; s4, extracting the blackheads by using a self-adaptive threshold method; and S5, utilizing the tkater to manufacture an interactive interface. The method adopts the convolutional neural network to segment the facial features, thereby avoiding the influence of color difference of different parts on a detection result, extracting a single-channel image to reduce the influence of illumination and skin color, removing the influence of the border, nostril shadow and highlight by adopting a corrosion and threshold segmentation method, and detecting the influence of factors such as illumination reduction by adopting a self-adaptive threshold.

Description

Nose wing blackhead image detection method based on deep learning and adaptive threshold method
Technical Field
The invention belongs to the technical field of digital image processing, and particularly relates to a method for detecting a blackhead image of a nasal wing based on deep learning and an adaptive threshold method.
Background
Digital Image Processing (Digital Image Processing) is a method and technique for performing processes such as denoising, enhancement, restoration, segmentation, feature extraction, and the like on an Image by a computer. The generation and rapid development of digital image processing is largely influenced by three factors: the development of computers; secondly, the development of mathematics (particularly the establishment and perfection of discrete mathematical theory); and thirdly, the application requirements of wide agriculture and animal husbandry, forestry, environment, military, industry, medicine and the like are increased.
Deep Learning (DL) is a new research direction in the field of Machine Learning (ML), which is introduced into Machine Learning to make it closer to the original target, Artificial Intelligence (AI). Deep learning is the intrinsic law and expression level of the learning sample data, and the information obtained in the learning process is very helpful for the interpretation of data such as characters, images and sounds. The final aim of the method is to enable the machine to have the analysis and learning capability like a human, and to recognize data such as characters, images and sounds. Deep learning is a complex machine learning algorithm, and achieves the effect in speech and image recognition far exceeding the prior related art. Deep learning has achieved many achievements in search technology, data mining, machine learning, machine translation, natural language processing, multimedia learning, speech, recommendation and personalization technologies, and other related fields. The deep learning enables the machine to imitate human activities such as audio-visual and thinking, solves a plurality of complex pattern recognition problems, and makes great progress on the artificial intelligence related technology.
Convolutional Neural Networks (CNN) are a class of feed forward Neural Networks (fed forward Neural Networks) that contain convolution computations and have a deep structure, and are one of the representative algorithms for deep learning (deep learning). Convolutional Neural Networks have a feature learning (rendering) capability, and can perform Shift-Invariant classification (Shift-Invariant classification) on input information according to a hierarchical structure thereof, and are therefore also called "Shift-Invariant Artificial Neural Networks (SIANN)". The convolutional neural network is constructed by imitating a visual perception (visual perception) mechanism of a living being, can perform supervised learning and unsupervised learning, and has the advantages that the convolutional neural network can learn grid-like topologic features such as pixels and audio with small calculation amount, has stable effect and has no additional feature engineering (feature engineering) requirement on data due to the fact that convolutional kernel parameter sharing in an implicit layer and sparsity of connection between layers.
In recent years, people pay more and more attention to skin care, and various skin detection products are also activated on the market. The black head is very difficult to detect because the black head has small proportion in the photo, unobvious color and is greatly influenced by factors such as illumination, spots on the skin, camera pixels and the like.
Disclosure of Invention
In order to solve the problems, the invention discloses a nose wing blackhead image detection method based on deep learning and an adaptive threshold method, which is characterized in that a convolutional neural network is adopted to segment facial features, so that the influence of color difference of different parts on a detection result is avoided, a single-channel image is extracted to reduce the influence of illumination and skin color, the influence of boundary, nostril shadow and highlight is removed by adopting a corrosion and threshold segmentation method, and the influence of factors such as illumination reduction of a blackhead is detected by adopting adaptive threshold and the like. And finally, making a user interaction interface for use.
The above purpose is realized by the following technical scheme:
a nose wing blackhead image detection method based on deep learning and an adaptive threshold method comprises the following steps:
s1, collecting a facial feature segmentation data set, training a CNN cascade network and storing network parameters;
s2, calling the trained CNN cascade network to perform facial feature segmentation, and extracting a nose image in the face image;
s3, extracting a blue single channel from the nose image, removing the influence of edges, light rays and shadows, and removing the nostril part;
s4, extracting the blackheads by using a self-adaptive threshold method;
and S5, utilizing the tkater to manufacture an interactive interface.
In the method for detecting the blackhead images of the nose wings based on the deep learning and adaptive threshold method, the CNN cascade network detects the facial markers by using a cross entropy loss function in step S1, 68 detected locating points are coded into 68 independent channels, the two-dimensional Gaussian distribution is arranged at the corresponding facial marker positions of the channels, and the 68 channels are stacked together and transmitted to a region segmentation network together with an original image;
the training CNN cascade network encodes the marks into two-dimensional Gaussian distribution at the positions of the provided facial marks, each facial mark is allocated with a separate channel to prevent the marks from being overlapped with other facial marks, in the cross entropy loss function, n is a label of the labeled data, and the Gaussian distribution of the labeled data
Figure BDA0003076106000000021
And predicted Gaussian distribution
Figure BDA0003076106000000022
Have the same dimensions: n W H, N being the number of channels, W being the width, H being the height, the cross entropy loss function defined is:
Figure BDA0003076106000000023
face region segmentation network, using softmax loss function as the final loss function:
Figure BDA0003076106000000024
where M is the number of outputs.
In the method for detecting the blackhead image of the nasal wing by the deep learning and adaptive threshold method, the specific method for extracting the blackhead by the adaptive threshold method in step S4 is as follows:
setting the threshold value as T, dividing the image into target images C1And a background image C2Two parts, traversing the whole gray scale interval, determining a proper threshold value T, and enabling C1、C2The difference between the gray value variance of the two parts is maximum;
for a given image I (x, y), let T be the threshold for segmenting the target image and the background image, and let ω be the ratio of the number of pixels belonging to the target image to the total image0Average gray of μ0(ii) a The proportion of the pixel points belonging to the background image to the whole image is omega1Average gray of μ1(ii) a The total average gray level of the image is mu, and the inter-class variance is g; the size of the image I (x, y) is M N, and the number of pixels in the image with a gray scale value less than the threshold value T is denoted as N0The number of pixels with gray values greater than the threshold T is denoted by N1Then, there are:
Figure BDA0003076106000000031
Figure BDA0003076106000000032
N0+N1＝M×N
ω0+ω1＝1
μ＝ω0μ0+ω1μ1
g＝ω0(μ0-μ)2+ω1(μ0-μ)2
thereby obtaining:
g＝ω0ω1(μ0-μ1)2
the segmentation threshold that maximizes the inter-class variance is obtained by traversing the entire interval.
The invention has the beneficial effects that:
the method uses the convolutional neural network to segment the facial features, thereby avoiding the influence of color difference of different parts on a detection result, extracts a single-channel image to reduce the influence of illumination and skin color, removes the influence of boundary, nostril shadow and highlight by adopting a corrosion and threshold segmentation method, and detects the influence of factors such as illumination reduction of a blackhead by adopting a self-adaptive threshold and the like. The invention can accurately and quickly detect the blackheads of the nose wings on the face image, provides skin care suggestions for a detector, and provides certain technical reference for the fields of medical cosmetology and the like.
Drawings
FIG. 1 is a face segmentation result;
FIG. 2 is a segmented nose image;
FIG. 3 is a graph showing the results of blackhead detection;
FIG. 4 is a user interface: selecting a picture address and saving the address;
FIG. 5 is a user interface: detecting the result;
FIG. 6 is a user interface: and detecting picture display.
Detailed Description
The present invention will be further illustrated with reference to the accompanying drawings and specific embodiments, which are to be understood as merely illustrative of the invention and not as limiting the scope of the invention.
The invention discloses a method for detecting a blackhead image of a nasal wing based on deep learning and an adaptive threshold method, which comprises the following steps:
s1, collecting a facial feature segmentation data set, training a CNN cascade network and storing network parameters;
the CNN cascade network utilizes a cross entropy loss function to detect the facial markers, encodes the detected 68 positioning points into 68 independent channels, the channels have two-dimensional Gaussian distribution at the corresponding facial marker positions, and the 68 channels are stacked together and transmitted to the region segmentation network together with the original image;
the training CNN cascade network encodes the marks into two-dimensional Gaussian distribution at the positions of the provided facial marks, each facial mark is allocated with a separate channel to prevent the marks from being overlapped with other facial marks, each point is easier to distinguish, in the cross entropy loss function, n is a label of the labeled data, and the Gaussian distribution of the labeled data
Figure BDA0003076106000000041
And predicted Gaussian distribution
Figure BDA0003076106000000042
Have the same dimensions: n W H, N being the number of channels, W being the width, H being the height, the cross entropy loss function defined is:
Figure BDA0003076106000000043
face region segmentation network, using softmax loss function as the final loss function:
Figure BDA0003076106000000044
where M is the number of outputs.
S2, calling the trained CNN cascade network to perform facial feature segmentation, and extracting a nose image in the face image;
and S3, extracting a blue single channel from the nose image, and removing the boundary and nostril shadows by adopting a corrosion and threshold segmentation method. Since the nostril shadow is a continuous area with the maximum gray value in the nose image, the nostril shadow can be removed by adopting a threshold segmentation method;
s4, extracting the blackheads by using a self-adaptive threshold method;
the adaptive threshold method is an improvement on the variance method between the largest classes. The idea is not to calculate the threshold of the global image, but to calculate the local threshold according to the brightness distribution of different areas of the image, so that the image is not subjected toIn the same region, different thresholds can be calculated in a self-adaptive mode. Setting the threshold value as T, dividing the image into target images C1And a background image C2Two parts, traversing the whole gray scale interval, determining a proper threshold value T, and enabling C1、C2The difference between the gray value variance of the two parts is maximum;
for a given image I (x, y), let T be the threshold for segmenting the target image and the background image, and let ω be the ratio of the number of pixels belonging to the target image to the total image0Average gray of μ0(ii) a The proportion of the pixel points belonging to the background image to the whole image is omega1Average gray of μ1(ii) a The total average gray level of the image is mu, and the inter-class variance is g; the size of the image I (x, y) is M N, and the number of pixels in the image with a gray scale value less than the threshold value T is denoted as N0The number of pixels with gray values greater than the threshold T is denoted by N1Then, there are:
Figure BDA0003076106000000045
Figure BDA0003076106000000051
N0+N1＝M×N
ω0+ω1＝1
μ＝ω0μ0+ω1μ1
g＝ω0(μ0-μ)2+ω1(μ0-μ)2
thereby obtaining:
g＝ω0ω1(μ0-μ1)2
the segmentation threshold that maximizes the inter-class variance is obtained by traversing the entire interval.
And finally, manufacturing an interactive interface by using the tkiner library, calculating the black head ratio, and providing skin care suggestions for the user. The interactive interface has the functions of image address selection, address saving selection, image display, starting detection, system quitting, prompting and the like.
The technical means disclosed in the invention scheme are not limited to the technical means disclosed in the above embodiments, but also include the technical scheme formed by any combination of the above technical features.

Claims (3)

1. A nose wing blackhead image detection method based on deep learning and an adaptive threshold method is characterized by comprising the following steps:
s1, collecting a facial feature segmentation data set, training a CNN cascade network and storing network parameters;
s2, calling the trained CNN cascade network to perform facial feature segmentation, and extracting a nose image in the face image;
s3, extracting a blue single channel from the nose image, removing the influence of edges, light rays and shadows, and removing the nostril part;
s4, extracting the blackheads by using a self-adaptive threshold method;
and S5, utilizing the tkater library to manufacture an interactive interface.
2. The method for detecting blackhead images of nasal wings based on deep learning and adaptive threshold method as claimed in claim 1, wherein the CNN cascade network performs facial landmark detection by using cross entropy loss function in step S1, encodes the detected 68 anchor points into 68 separate channels, the channels have a two-dimensional gaussian distribution at their corresponding facial landmark positions, and the 68 channels are stacked together and transmitted to the region segmentation network together with the original image;
the training CNN cascade network encodes the marks into two-dimensional Gaussian distribution at the positions of the provided facial marks, each facial mark is allocated with a separate channel to prevent the marks from being overlapped with other facial marks, in the cross entropy loss function, n is a label of the labeled data, and the Gaussian distribution of the labeled data
Figure FDA0003076105990000011
And predicted Gaussian distribution
Figure FDA0003076105990000012
Have the same dimensions: n W H, N being the number of channels, W being the width, H being the height, the cross entropy loss function defined is:
Figure FDA0003076105990000013
face region segmentation network, using softmax loss function as the final loss function:
Figure FDA0003076105990000014
where M is the number of outputs.
3. The method for detecting blackheads in nose wings based on deep learning and adaptive thresholding as claimed in claim 1, wherein the specific method for extracting blackheads by adaptive thresholding in step S4 is:
setting the threshold value as T, dividing the image into target images C1And a background image C2Two parts, traversing the whole gray scale interval, determining a proper threshold value T, and enabling C1、C2The difference between the gray value variance of the two parts is maximum;
for a given image I (x, y), let T be the threshold for segmenting the target image and the background image, and let ω be the ratio of the number of pixels belonging to the target image to the total image0Average gray of μ0(ii) a The proportion of the pixel points belonging to the background image to the whole image is omega1Average gray of μ1(ii) a The total average gray level of the image is mu, and the inter-class variance is g; the size of the image I (x, y) is M N, and the number of pixels in the image with a gray scale value less than the threshold value T is denoted as N0The number of pixels with gray values greater than the threshold T is denoted by N1Then, there are:
Figure FDA0003076105990000021
Figure FDA0003076105990000022
N0+N1＝M×N
ω0+ω1＝1
μ＝ω0μ0+ω1μ1
g＝ω0(μ0-μ)2+ω1(μ0-μ)2thereby obtaining:
g＝ω0ω1(μ0-μ1)2
the segmentation threshold that maximizes the inter-class variance is obtained by traversing the entire interval.
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