CN112084965A - Scalp hair detection device and system - Google Patents
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Abstract
The invention discloses a scalp hair detection device and system, the device includes: the device comprises an image acquisition module, a feature recognition module and a result acquisition module. The scalp hair detection device identifies the characteristics of the scalp hair image through the preset characteristic identification model by acquiring the image acquired by the detection host, then inputs the identified characteristic information into the preset scalp hair state identification model, and the preset scalp hair state identification model outputs a scalp hair state result. Compared with the prior art that the results of single-point monitoring and manual interpretation of the scalp hair state are adopted, the scalp hair detection method obtains the scalp hair detection result by obtaining the characteristic information of the scalp hair image of each area through the spectrum identification technology for the scalp hair image and processing the characteristic information, overcomes the defect that the scalp hair state cannot be objectively and accurately obtained in the prior art, and objectively and accurately realizes the scalp hair state detection.
Description
Technical Field
The invention relates to the field of scalp hair detection, in particular to a scalp hair detection device and system.
Background
The scalp belongs to one of sensitive skins of human bodies, and a large number of people who suffer from problems of scalp and hair in life have the problems of scalp allergy, folliculitis, dandruff, alopecia and the like. In reality, only a few individuals with severe scalp hair problems will seek the help of doctors, and most consumers will choose to purchase scalp hair care products for improving the quality of the scalp or hair. Some shampoo in the market is a product with high chemical irritation, the hair follicles on the scalp are damaged by chemical substances after long-term use of the product, and partial residues can cause accumulation and blockage on the scalp, so that various scalp hair problems are caused.
Many hair salon practitioners and hair management centers are linked in the market, and most of detections for hair are to take pictures of scalp in a single-point picture taking mode and obtain the state of scalp hair of a testee in an artificial reading mode. How to objectively and accurately detect the state of scalp hair is an urgent problem to be solved.
The above is only for the purpose of assisting understanding of the technical aspects of the present invention, and does not represent an admission that the above is prior art.
Disclosure of Invention
The invention mainly aims to provide a scalp and hair detection device and system, and aims to solve the technical problems that in the prior art, the scalp and hair detection result is easily influenced by subjective factors, and the detection is inaccurate, so that consumers cannot know the scalp and hair states of the consumers.
In order to achieve the above object, the present invention provides a scalp hair detecting device including:
the image acquisition module is used for acquiring a scalp and hair image acquired by the detection host;
the characteristic identification module is used for identifying the characteristics of the scalp hair images through a preset characteristic identification model so as to obtain characteristic information corresponding to each scalp hair image;
and the result acquisition module is used for inputting the characteristic information into a preset scalp hair state identification model and acquiring a scalp hair detection result output by the model.
Preferably, the image acquisition module is further configured to acquire a panoramic image of the head acquired by the electronic device;
the characteristic identification module is further configured to obtain a hair root distance between adjacent hair roots in the scalp hair image according to the scalp hair image and the head panoramic image, and compare the hair root distance with a preset hair root sparse distance to obtain a comparison result;
and the result acquisition module is also used for determining a scalp hair detection result according to the comparison result.
Preferably, the feature recognition module is further configured to determine a hair root position of each hair according to the scalp hair image, determine adjacent hair roots in the scalp hair image according to the hair root position, and acquire a hair root distance between the adjacent hair roots.
Preferably, the apparatus further comprises: a model training module;
the model training module is used for obtaining a model training set, carrying out iterative training on an initial characteristic recognition model or an initial scalp hair state recognition model through a deep learning algorithm according to the model training set, and obtaining a preset characteristic recognition model or a preset scalp hair state recognition model, wherein the model training set comprises characteristic information images of different scalp hairs.
Preferably, the feature information includes: scalp characteristic information or hair characteristic information;
correspondingly, the scalp and hair detection result comprises: a scalp test result or a hair test result;
the result acquisition module is used for inputting the scalp characteristic information into a preset scalp hair state identification model and acquiring a scalp detection result output by the model;
or the result acquisition module is used for inputting the hair characteristic information into a preset scalp hair state identification model and acquiring a hair detection result output by the model.
Preferably, the apparatus further comprises: an image optimization module;
the image optimization module is used for denoising the scalp hair images of all the areas through a Gaussian blur algorithm to obtain denoised images corresponding to the scalp hair images, and optimizing the denoised images corresponding to the scalp hair images of all the areas through a sharpening algorithm to obtain optimized images of the scalp hair images;
correspondingly, the characteristic identification module is used for identifying the optimized image of the scalp hair image of each area through a preset spectrum identification technology and acquiring the characteristic information corresponding to the scalp hair image of each area.
Preferably, the apparatus further comprises: a storage module; the storage module is used for storing the scalp detection result or the hair detection result.
In addition, the invention also provides a scalp hair detection system which comprises the scalp hair detection device.
Preferably, the scalp and hair detection system further comprises: detecting a host and an electronic device;
the detection host is used for acquiring a scalp hair image and sending the scalp hair image to the scalp hair detection device;
the electronic equipment is used for collecting the head panoramic image and sending the scalp hair image to the scalp hair detection device.
Preferably, the electronic device is further used for displaying the scalp and hair detection result.
The invention provides a scalp hair detection device and a scalp hair detection system. Compared with the prior art that the single-point monitoring and the scalp hair state manual interpretation result are adopted, the scalp hair detection method obtains the scalp hair detection result by obtaining the characteristic information of the scalp hair image by using the spectrum identification technology and processing the characteristic information, overcomes the defect that the scalp hair state cannot be objectively and accurately obtained in the prior art, and objectively and accurately realizes the scalp hair state detection.
Drawings
FIG. 1 is a block diagram showing the construction of a first embodiment of a scalp and hair measuring device according to the present invention;
FIG. 2 is a block diagram showing the construction of a scalp and hair measuring device according to a second embodiment of the present invention;
FIG. 3 is a block diagram showing the construction of a third embodiment of a scalp and hair measuring apparatus according to the present invention;
fig. 4 is a block diagram showing the structure of a first embodiment of a scalp hair detection system according to the present invention.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Referring to fig. 1, fig. 1 is a block diagram showing the structure of a scalp and hair measuring device according to a first embodiment of the present invention.
As shown in fig. 1, the scalp and hair detecting apparatus includes: the system comprises an image acquisition module 10, a feature recognition module 20 and a result acquisition module 30.
The image acquisition module 10 is configured to acquire a scalp and hair image acquired by the detection host.
It should be noted that, acquiring the scalp and hair image acquired by the detection host includes acquiring the scalp image and/or the hair image acquired by the detection host, where the images are multiple area images of the scalp or the hair.
With reference to fig. 4, it should be noted that the detection host 200 is a scalp hair image capturing device, the detection host 200 is connected to the scalp hair detection apparatus 100 in a wired or wireless manner, the detection host 200 includes a first photographing module 2001, and the first photographing module 2001 includes two replaceable lenses, a scalp magnifying lens and a hair magnifying lens. When the first photographing module 2001 collects scalp images, a scalp magnifier with preset multiples and the auxiliary light source group 2002 are used for collecting the scalp images, and three images under different light sources are collected at one time, wherein the scalp magnifier with the preset multiples can be a scalp magnifier with 50 times, a scalp magnifier with 70 times or scalp magnifiers with other multiples; the first photographing module 2001 collects images of the root, middle and tip of the hair using a hair magnifier with a preset multiple, wherein the hair magnifier with the preset multiple can be 200 times of hair magnifiers, 500 times of hair magnifiers or other multiples of hair magnifiers.
It is understood that the scalp hair detection device 100 needs to receive the scalp hair image acquired by the detection host 200 or acquire the scalp hair image acquired by the detection host 200 by sending an image acquisition instruction to the detection host 200, and then perform the subsequent operation.
It should be noted that the feature information includes: scalp characteristic information or hair characteristic information; the scalp hair detection result comprises the following steps: scalp test results or hair test results. When a scalp detection result is obtained, the result obtaining module inputs the scalp characteristic information into a preset scalp hair state identification model and outputs a scalp detection result; and when the hair detection result is obtained, the result obtaining module inputs the hair characteristic information into a preset scalp hair state identification model and outputs the hair detection result.
The feature recognition module 20 is configured to recognize features of the scalp hair images through a preset feature recognition model, so as to obtain feature information corresponding to each scalp hair image.
It should be noted that, in the present embodiment, the characteristics of the scalp hair image are identified by the three-spectrum identification technology, and when the detection host 200 collects the scalp hair image, the scalp hair image can be collected under different light sources.
When identifying the scalp type, the scalp type is mainly characterized by the oil stain on the scalp and the degree of the oil stain, so when identifying the scalp type characteristics, the oil stain on each area image and the degree of the oil stain are mainly identified.
When identifying dandruff, the main characteristic of dandruff is to find the area of white blocks around the hair area in the skin color area under the irradiation of daily light, because dandruff is mainly characterized by the presence of white blocks with different numbers on the scalp and has a high probability of accompanying the hair.
When scalp sensitivity is identified, namely anaphylactic reaction, a large blood red area is generated under the irradiation of polarized light when the scalp is sensitive, so that when the scalp sensitivity is identified, under the irradiation of the polarized light, the large blood red area is mainly searched in the scalp area.
When folliculitis is identified, a large number of small red dots are displayed under the irradiation of ultraviolet light when the scalp suffers from folliculitis, so that the area of a red block existing in a skin color area is mainly searched under the irradiation of ultraviolet light when the folliculitis is identified.
When identifying hair scales, the hair scales are bright and smooth under the irradiation of a single light source, so that when identifying the hair scales, the main purpose is to identify the brightness and smoothness of the hair under the irradiation of the single light source.
The result obtaining module 30 is configured to input the feature information corresponding to each scalp hair image into a preset scalp hair state identification model, and obtain a scalp hair detection result output by the model.
It should be noted that the preset scalp hair state recognition model is obtained by training an untrained neural network model through a deep learning algorithm.
It can be understood that the characteristic information corresponding to each scalp hair image is input into the preset scalp hair state identification model, and then the preset scalp hair state identification model outputs the characteristic information corresponding to each scalp hair image, so as to obtain the scalp hair detection result.
The specific detection result is obtained as follows: when a scalp type detection result is obtained, presetting a scalp hair state identification model, and outputting the types of the scalp in the image according to the oil stains appearing in the images of all the areas and the degree of the oil stains appearing: dry scalp, neutral scalp or oily scalp.
When a dandruff detection result is obtained, presetting a white block area around a hair area existing in a skin color area under the irradiation of daily light by a scalp hair state identification model, and outputting the dandruff abnormal degree in an image as follows: the degree of dandruff is severe, general or good.
When a sensitive, namely allergic reaction detection result is obtained, the number and the area of areas which can generate massive red blood are preset in the scalp hair state identification model according to the irradiation of polarized light, and the scalp sensitivity abnormal degree in an output image is as follows: the sensitivity is serious, general or good.
When a folliculitis detection result is obtained, presetting a scalp hair state identification model, wherein under the irradiation of ultraviolet light, a plurality of small red dots exist in a skin color area, and outputting a scalp folliculitis degree as follows: the degree of folliculitis is severe, the degree of folliculitis is general or the degree of folliculitis is good.
When the density detection result is obtained, the preset scalp hair state recognition model compares the hair root distance between the adjacent hair roots with the preset hair root sparse distance, and the output hair density detection result is as follows: thin hair, normal hair or dense hair.
When obtaining hair scale testing result, predetermine scalp hair state recognition model and shine down hair gloss degree and the smooth-going degree of hair according to single light source, output hair scale anomaly testing result is: hair scales are abnormal severe, normal or mild.
The embodiment provides a scalp hair detection device 100, which identifies the characteristics of a scalp hair image by acquiring an image acquired by a detection host 200 through a preset characteristic identification model, inputs the identified characteristic information into a preset scalp hair state identification model, and outputs a scalp hair state result according to the preset scalp hair state identification model. Compared with the prior art that single-point monitoring and manual interpretation of the scalp hair state results are adopted, the embodiment obtains the scalp hair detection results by obtaining the characteristic information of the scalp hair images through the spectrum identification technology and processing the characteristic information, overcomes the defect that the scalp hair state cannot be objectively and accurately obtained in the prior art, and objectively and accurately realizes the scalp hair state detection.
Fig. 2 is a block diagram showing the structure of a scalp and hair measuring device according to a second embodiment of the present invention, and the present invention is explained based on the first embodiment.
In the second embodiment, the scalp and hair detecting device further includes: a distance calculation module 40 and a distance comparison module 50.
The image obtaining module 10 is further configured to obtain a panoramic image of the head acquired by the electronic device 300.
With reference to fig. 4, it should be noted that the electronic device 300 is a device for capturing a panoramic image of a head, and with reference to fig. 4, the electronic device 300 includes a second camera module 3001, and the second camera module 3001 is used for capturing the panoramic image of the head.
It should be noted that the head panoramic image is used for simply determining the density of the hair. And obtaining whether the head of the testee has a serious result of local hair loss according to the head panoramic image. For example, a male testee can easily judge whether baldness exists according to the head panoramic image, and further can simply determine the density of hair.
The distance calculating module 40 is configured to calculate a euclidean distance between adjacent hair roots.
It should be noted that the euclidean distance is a commonly used distance definition, and refers to a real distance between two points in a multidimensional space, or a natural length of a vector, i.e., a distance from the point to an origin. The euclidean distance in two and three dimensions is the actual distance between two points.
It should be noted that, in this embodiment, when determining adjacent hair roots, a coordinate system may be established with any hair root as an origin, concentric circles with gradually increasing radii may be sequentially drawn with a minimum distance unit multiple of the coordinate system as a radius, and coordinates of the adjacent hair roots and each hair root in different directions of the hair root may be determined according to the drawn concentric circles.
It can be understood that, the calculation of the euclidean distance between adjacent hair roots is to calculate the euclidean distance between rural hair roots according to the determined coordinates of the adjacent hair roots and each hair root in the coordinate system and the euclidean distance in the multidimensional space. The mean value of the Euclidean distance between adjacent hair roots can be calculated according to the Euclidean distance between the adjacent hair roots in the whole area.
The distance comparison module 50 is configured to compare an average value of euclidean distances between adjacent hair roots with a preset hair root sparse distance.
It should be noted that the preset hair root thinning distance is a preset threshold, and the threshold can divide the hair root into three levels of thinning, normal and dense. For example, the preset hair root sparse distance is [ 0.1-0.3 mm ] including end point values, the average value of the Euclidean distances between adjacent hair roots is calculated, whether the average value of the Euclidean distances is smaller than 0.1mm, belongs to 0.1-0.3 mm or is larger than 0.3mm or not is compared, and the comparison result of the average value of the Euclidean distances between the adjacent hair roots and the preset hair root sparse distance can be obtained.
The result obtaining module 40 may obtain the result of the density of the hair according to the comparison result.
The embodiment provides a scalp hair detection device 100, which obtains a scalp hair image collected by a detection host 200 and a head panoramic image collected by an electronic device 300, performs feature recognition, calculation and comparison on an area image and the panoramic image, and then obtains a result of hair density. Compared with the prior art that single-point monitoring is adopted and scalp state results are manually read, the embodiment obtains the hair density detection result by obtaining the characteristic information of each area by utilizing the spectrum identification technology for the scalp and hair images and calculating and comparing the characteristic information, and overcomes the defect that the scalp and hair states cannot be objectively and accurately obtained in the prior art, so that the scalp and hair state detection is objectively and accurately realized.
As shown in fig. 3, fig. 3 is a block diagram showing the structure of a scalp and hair detection device according to a third embodiment of the present invention, and the third embodiment is proposed based on the first embodiment and the second embodiment, and is explained based on the first embodiment.
In the third embodiment, the scalp and hair detecting apparatus further includes: a model training module 60, an image optimization module 70, and a storage module 80.
The model training module 60 is configured to obtain a model training set, perform iterative training on the initial feature recognition model or the initial scalp hair state recognition model through a deep learning algorithm according to the model training set, and obtain a preset feature recognition model or a preset scalp hair state recognition model, where the model training set includes feature information images of different scalp hairs.
It is understood that the model training set is a collection of characteristic information images of different scalp hairs. The images in the set comprise characteristic information images of scalp hair with different detection dimensions, and the specific images comprise: hair density images of three dimensions of sparse, normal and dense; scalp type images at three latitudes, dry, neutral and oily; dandruff, scalp sensitivity and scalp folliculitis images at three latitudes, severe, normal and good; hair scale images at three latitudes, severe, normal and mild; at least 500 images per dimension level.
It should be noted that the deep learning algorithm is machine learning based on a neural network. Compared with the traditional machine learning, the deep learning needs more samples, and fewer manual labels and higher accuracy are provided. The deep learning utilizes depth to replace breadth, parameters are further reduced, fitting ability is improved, and the deep learning algorithm machine is better than a traditional algorithm learning machine in most cases. In the embodiment, the model is trained according to a convolutional neural network, which is a kind of feedforward neural network containing convolution calculation and having a deep structure, and is one of the representative algorithms of deep learning. The iterative method, also called rolling method, is a process of continuously recursing new value by using old value of variable, and is a direct method corresponding to the iterative method, i.e. the problem is solved once. The iterative algorithm is a basic method for solving the problem by using a computer, and utilizes the characteristics of high operation speed and suitability for repetitive operation of the computer to make the computer repeatedly execute a group of instructions or a certain step, and when the group of instructions or the steps are executed each time, a new value of the instructions or the steps is deduced from an original value of a variable.
It should be noted that the model training module 60 includes a training unit and a detection unit, the training unit performs recognition training on the feature recognition model through the neural network model, and performs training on the scalp and hair state recognition model through the deep learning algorithm, the detection unit detects the accuracy of the recognition results of the feature recognition model and the scalp and hair state recognition model, and continues to perform recognition training on the scalp and hair state recognition model by adopting an iteration method under the condition of low accuracy until the accuracy of the recognition results of the feature recognition model and the scalp and hair state recognition model reaches the standard, and the scalp and hair state recognition model with the accuracy reaching the standard is used as the preset scalp and hair state recognition model.
The image optimization module 70 performs noise reduction on each scalp hair image through a gaussian fuzzy algorithm to obtain noise-reduced images of each scalp hair image, and then optimizes the noise-reduced images of each scalp hair image through a sharpening algorithm to obtain optimized images of each scalp hair image.
It should be noted that "blur" in the gaussian blur algorithm is understood to mean that each pixel is an average value of surrounding pixels. Since images are continuous, closer points are more closely related and farther points are more distant, it is more reasonable to use weighted averaging here, where closer points are weighted more heavily and farther points are weighted less heavily. And denoising the scalp hair images through a Gaussian fuzzy algorithm to obtain the noise-reduced images of the scalp hair images.
It should be noted that the sharpening algorithm is to subtract the original image from the noise-reduced image obtained by the gaussian blur algorithm to obtain some edge contour information and detail information, and then add the edge contour information and the detail information to the original image, thereby sharpening the feature information. And sharpening the characteristic information of each scalp hair image through a sharpening algorithm to obtain each scalp hair image optimized image with the characteristic information highlighted after sharpening.
The storage module 80 is configured to store the scalp test result or the hair test result.
It should be noted that the storage module 80 is used for storing the scalp test result and/or the hair test result.
It should be noted that, the scalp test result and the hair test result stored in the storage module 80 can be called and displayed through a software platform installed in the electronic device 300. For example, a certain detector can query the previous scalp hair state detection result by inputting corresponding information on a software platform installed on a smart phone in a hand, and then compare a plurality of scalp hair state detection results, so that a scalp hair state improvement result can be obviously obtained.
Before scalp hair detection is performed, the examiner needs to input related feature information so that the scalp hair detection result can be classified and stored and the electronic device 300 can call the scalp hair detection result.
The embodiment provides a scalp hair detection device 100, which performs feature recognition on an acquired image by acquiring the image acquired by a detection host 200, inputs the recognized feature information into a preset scalp hair state recognition model, and outputs a result according to the preset scalp hair state recognition model. Compared with the prior art that single-point monitoring and manual interpretation of scalp hair state results are adopted, the embodiment obtains the scalp hair detection results by obtaining the characteristic information of each area through the spectrum identification technology for the scalp hair image and processing the characteristic information, and overcomes the defect that the prior art cannot objectively and accurately obtain the scalp hair state, so that the scalp hair state detection is objectively and accurately realized.
Referring to fig. 4, fig. 4 is a block diagram illustrating a first embodiment of a scalp hair detection system according to the present invention.
The scalp hair state detection system comprises: the scalp hair detection device 100, the detection host machine 200 and the electronic device 300 are connected with each other, so that data transmission can be realized.
The scalp and hair detecting apparatus 100 is specifically described in the first, second and third embodiments, and will not be described herein.
The detection host 200 includes a first photographing module 2001, an auxiliary light source group 2002, and a sending module 2003. The first photographing module 2001 includes two replaceable lenses, a scalp magnifying lens and a hair magnifying lens. The auxiliary light source group 2002 is a light source group that can emit normal light, polarized light, and ultraviolet light. When the detection host 200 collects scalp images, a scalp magnifier and an auxiliary light source group 2002 with preset multiples are used for collecting the scalp images, and three images under different light sources are collected at one time, wherein the scalp magnifier with the preset multiples comprises a scalp magnifier with 50 times, a scalp magnifier with 70 times or scalp magnifiers with other multiples; when gathering the hair image, use the enlarged hair mirror of preset times to gather in hairy root, the hair and the tip of sending out of hair, the enlarged hair mirror of preset times includes the enlarged hair mirror of 200 times, the enlarged hair mirror of 500 times or other multiples. The sending module 2003 is configured to send the collected scalp hair image to the scalp hair detection device 100.
It should be noted that the sending module 2003 may perform wireless connection between the detection host 200 and the electronic device 300 through a WIFI module, or may perform wired connection through an interface module.
It should be noted that, when the examiner is examining the scalp hair state, the first photographing module 2001 is equipped with a scalp magnifier 50 times that of the examiner, and the auxiliary light source group 2002 is turned on to collect the scalp hair state, and different parts are photographed under each light source, and three images are collected at a time; when the hair image is captured, the first photographing module 2001 is provided with the hair magnifier 200 to capture the roots, middle and tips of the hair, and three images are captured at a time.
The sending module 2003 converts image data into an electrical signal using an optical system of a cmos chip and sends the electrical signal. An optical system of a cmos chip, which is one of solid-state image sensors, is a device that converts an image signal into an electric signal for output, which is made of a semiconductor material, and operates on the principle of charge storage. The p-n junction is reversely charged and then discharged under the illumination condition, the discharge speed is different along with the difference of the illumination intensity, and after a period of discharge, the charges reserved on each pixel are different, so that the photoelectric conversion is realized, the image signals are gathered on the p-n junction pixel array surface by an optical system to scan the pixel array one by one, and the electric signals of an image can be obtained.
It should be noted that the detection host 200 further includes: the device comprises a switch button, a collection button and a starting indicator light. In actual operation, the power switch of the detection host 200 is pressed, when the start indicator of the housing is constantly on, the start of the detection host 200 is completed, and then the collection button is pressed to collect the scalp and hair images.
The electronic device 300 includes a second camera module 3001, a sending module 3002 and a display module 3003, and may be a smart phone, a computer or other devices having the functions, which are not limited herein. The second photographing module 3001 is configured to capture a head panoramic image, where the head panoramic image is based on hair resolution; the transmission module 3002 is used for data transmission between the electronic device 300 and the scalp and hair detection apparatus 100; the display module 3003 is used for displaying the scalp hair detection result.
It should be noted that data transmission between the electronic device 300 and the scalp hair detection apparatus 100 is mutual, the electronic device 300 may transmit the collected head panoramic image to the scalp hair detection apparatus 100, and the scalp hair detection apparatus 100 may transmit the result of the hair and scalp detection to the electronic device 300, and then the result is displayed by the display module 3003 of the electronic device 300.
It should be noted that, the electronic device 300 is equipped with a corresponding software platform, and when acquiring the scalp hair image, the acquisition may be implemented by an acquisition button on the detection host 200, or the acquisition may be implemented at a remote end on the electronic device 300 by the software platform.
The application provides a scalp hair detecting system, by detecting host computer 200 and electronic equipment 300 carry out image acquisition, scalp hair detection device 100 carries out feature recognition to the image of acquireing, then inputs the characteristic information who recognizes to predetermineeing in the scalp hair state identification model, according to predetermineeing scalp hair state identification model output scalp hair state result, and feed back to electronic equipment 300 and demonstrate. Compared with the prior art that single-point monitoring is adopted and the scalp hair state result is manually read, the embodiment obtains the scalp hair detection result by obtaining the characteristic information of each area image by utilizing the three-spectrum recognition technology for the scalp hair image and processing the characteristic information, and overcomes the defect that the scalp state cannot be objectively and accurately obtained in the prior art, so that the scalp hair state detection is objectively and accurately realized.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or system that comprises the element.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which is stored in a storage medium (e.g., a rom/ram, a magnetic disk, an optical disk) and includes instructions for enabling a terminal device (e.g., a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method according to the embodiments of the present invention.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (10)
1. A scalp hair detecting device characterized by comprising:
the image acquisition module is used for acquiring a scalp and hair image acquired by the detection host;
the characteristic identification module is used for identifying the characteristics of the scalp hair images through a preset characteristic identification model so as to obtain characteristic information corresponding to each scalp hair image;
and the result acquisition module is used for inputting the characteristic information into a preset scalp hair state identification model and acquiring a scalp hair detection result output by the model.
2. The apparatus of claim 1, wherein the image obtaining module is further configured to obtain a head panorama image captured by an electronic device;
the characteristic identification module is further configured to obtain a hair root distance between adjacent hair roots in the scalp hair image according to the scalp hair image and the head panoramic image, and compare the hair root distance with a preset hair root sparse distance to obtain a comparison result;
and the result acquisition module is also used for determining a scalp hair detection result according to the comparison result.
3. The apparatus of claim 2, wherein the feature recognition module is further configured to determine a hair root position of each hair from the scalp hair image, determine adjacent hair roots in the scalp hair image from the hair root position, and obtain a hair root distance between the adjacent hair roots.
4. The apparatus of claim 1, wherein the apparatus further comprises: a model training module;
the model training module is used for obtaining a model training set, carrying out iterative training on an initial characteristic recognition model or an initial scalp hair state recognition model through a deep learning algorithm according to the model training set, and obtaining a preset characteristic recognition model or a preset scalp hair state recognition model, wherein the model training set comprises characteristic information images of different scalp hairs.
5. The apparatus of claim 1, wherein the characteristic information comprises: scalp characteristic information or hair characteristic information;
correspondingly, the scalp and hair detection result comprises: a scalp test result or a hair test result;
the result acquisition module is used for inputting the scalp characteristic information into a preset scalp hair state identification model and acquiring a scalp detection result output by the model;
or the result acquisition module is used for inputting the hair characteristic information into a preset scalp hair state identification model and acquiring a hair detection result output by the model.
6. The apparatus of any of claims 1-5, further comprising: an image optimization module;
the image optimization module is used for denoising the scalp hair images of all the areas through a Gaussian blur algorithm to obtain denoised images corresponding to the scalp hair images, and optimizing the denoised images corresponding to the scalp hair images of all the areas through a sharpening algorithm to obtain optimized images of the scalp hair images;
correspondingly, the characteristic identification module is used for identifying the optimized image of the scalp hair image of each area through a preset spectrum identification technology and acquiring the characteristic information corresponding to the scalp hair image of each area.
7. The apparatus of claim 5, wherein the apparatus further comprises: a storage module; the storage module is used for storing the scalp detection result or the hair detection result.
8. A scalp hair detection system characterized in that it comprises a scalp hair detection device according to any one of claims 1 to 7.
9. The system of claim 8, wherein the system further comprises: detecting a host and an electronic device;
the detection host is used for acquiring a scalp hair image and sending the scalp hair image to the scalp hair detection device;
the electronic equipment is used for collecting the head panoramic image and sending the scalp hair image to the scalp hair detection device.
10. The system of claim 9, wherein the electronic device is further configured to present scalp hair test results.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115346681A (en) * | 2022-08-29 | 2022-11-15 | 北京云数智康医疗科技有限公司 | AI evaluation analysis and calculation system and method for survival rate of head hair transplantation operation |
| CN116664585A (en) * | 2023-08-02 | 2023-08-29 | 瑞茜时尚(深圳)有限公司 | Scalp health condition detection method and related device based on deep learning |
| TWI823143B (en) * | 2021-09-13 | 2023-11-21 | 美科實業股份有限公司 | Intelligent dandruff detection system and method |
| CN117274155A (en) * | 2023-07-26 | 2023-12-22 | 苏州城市学院 | Method and system for detecting and extracting text characters in weld joint radial image |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102103690A (en) * | 2011-03-09 | 2011-06-22 | 南京邮电大学 | Method for automatically portioning hair area |
| CN106611160A (en) * | 2016-12-15 | 2017-05-03 | 中山大学 | CNN (Convolutional Neural Network) based image hair identification method and device |
| CN109074854A (en) * | 2016-03-30 | 2018-12-21 | 夏普株式会社 | computer, information processing method and network system |
| CN109949279A (en) * | 2019-03-06 | 2019-06-28 | 义乌市悦美科技有限公司 | A kind of Analysis of age system based on skin image |
| CN110298393A (en) * | 2019-06-14 | 2019-10-01 | 深圳志合天成科技有限公司 | A kind of hair scalp health conditions detection method based on deep learning |
| CN111428655A (en) * | 2020-03-27 | 2020-07-17 | 厦门大学 | Scalp detection method based on deep learning |
-
2020
- 2020-09-11 CN CN202010956642.4A patent/CN112084965A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102103690A (en) * | 2011-03-09 | 2011-06-22 | 南京邮电大学 | Method for automatically portioning hair area |
| CN109074854A (en) * | 2016-03-30 | 2018-12-21 | 夏普株式会社 | computer, information processing method and network system |
| CN106611160A (en) * | 2016-12-15 | 2017-05-03 | 中山大学 | CNN (Convolutional Neural Network) based image hair identification method and device |
| CN109949279A (en) * | 2019-03-06 | 2019-06-28 | 义乌市悦美科技有限公司 | A kind of Analysis of age system based on skin image |
| CN110298393A (en) * | 2019-06-14 | 2019-10-01 | 深圳志合天成科技有限公司 | A kind of hair scalp health conditions detection method based on deep learning |
| CN111428655A (en) * | 2020-03-27 | 2020-07-17 | 厦门大学 | Scalp detection method based on deep learning |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| TWI823143B (en) * | 2021-09-13 | 2023-11-21 | 美科實業股份有限公司 | Intelligent dandruff detection system and method |
| CN115346681A (en) * | 2022-08-29 | 2022-11-15 | 北京云数智康医疗科技有限公司 | AI evaluation analysis and calculation system and method for survival rate of head hair transplantation operation |
| CN117274155A (en) * | 2023-07-26 | 2023-12-22 | 苏州城市学院 | Method and system for detecting and extracting text characters in weld joint radial image |
| CN116664585A (en) * | 2023-08-02 | 2023-08-29 | 瑞茜时尚(深圳)有限公司 | Scalp health condition detection method and related device based on deep learning |
| CN116664585B (en) * | 2023-08-02 | 2023-11-24 | 瑞茜时尚(深圳)有限公司 | Scalp health condition detection method and related device based on deep learning |
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