CN113283446B - Method and device for identifying object in image, electronic equipment and storage medium - Google Patents
Method and device for identifying object in image, electronic equipment and storage medium Download PDFInfo
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Abstract
The invention relates to an image processing technology, and discloses a method for identifying an object in an image, which comprises the following steps: obtaining a sample image set through image amplification; constructing a first training set and a testing set by using the noise image and the sample image set, and training an original recognition model by using the first training set to obtain an initial recognition model; testing the initial recognition model by using a test set, selecting an error test result to construct a second training set so as to generate a first feature vector, and constructing a second feature vector of the sample image; and calculating a loss value between the first feature vector and the second feature vector to update parameters of the initial recognition model so as to obtain a standard recognition model to recognize the image to be recognized and obtain a target object recognition result in the image. In addition, the invention also relates to a blockchain technology, and the sample image can be stored in nodes of the blockchain. The invention also provides a device, equipment and medium for identifying the target in the image. The invention can solve the problem of lower accuracy of identifying the target object.
Description
Technical Field
The present invention relates to the field of image processing technologies, and in particular, to a method and apparatus for identifying an object in an image, an electronic device, and a computer readable storage medium.
Background
With the rapid development of artificial intelligence technology, people often use an image recognition model to process images to recognize information of objects contained in the images, for example, use an image recognition model machine to analyze disease images to determine types of lesions in the images, etc. in daily life. But the number of images available for training the model is very small due to the privacy of the medical images, which in turn results in a low accuracy of the trained model.
In the existing model training for a small number of samples, an original small number of samples are often expanded into a plurality of images by adopting an image expansion mode, so that a large number of models are trained. However, in the method, since the new images generated by image expansion are obtained based on the features of the original image, the basic features of the images in the new images are not changed, so that the situations of over fitting and the like of the trained model are easy to occur, and the accuracy of the model on object identification is not high.
Disclosure of Invention
The invention provides a method and a device for identifying an object in an image and a computer readable storage medium, and mainly aims to solve the problem of low accuracy in identifying the object.
In order to achieve the above object, the present invention provides a method for identifying an object in an image, including:
acquiring a sample image containing a target object, and performing image amplification on the sample image to obtain a sample image set;
constructing a first training set and a test set by using a noise image of the same type as the sample image and the sample image set, and training a pre-constructed original recognition model by using the first training set to obtain an initial recognition model;
testing the initial recognition model by using the test set, selecting a preset type of error result in the test result to construct a second training set, and constructing a third training set by using the sample image;
extracting a first feature vector of the image in the second training set, and extracting a second feature vector of the image in the third training set;
calculating a loss value between the first feature vector and the second feature vector, and carrying out parameter updating on the initial recognition model according to the loss value to obtain a standard recognition model;
And acquiring an image to be identified, and identifying a target object in the image to be identified by utilizing the standard identification model to obtain a target object identification result in the image.
Optionally, the performing image amplification on the sample image to obtain a sample image set includes:
performing texture drawing on the sample image to obtain an image texture of the sample image;
carrying out random local deepening on the image texture to obtain a texture deepened image;
randomly and locally desalting the image file to obtain a texture desalted image;
and collecting the texture deepening image and the texture fading image into the sample image set.
Optionally, the performing random local deepening on the image texture to obtain a texture deepened image includes:
counting the number of textures of the image textures;
selecting image textures with preset proportions as textures to be processed according to the number of the textures;
and adjusting pixel values of the pixels on the texture to be processed to obtain a texture deepened image.
Optionally, training the pre-built original recognition model by using the first training set to obtain an initial recognition model, including:
performing image recognition on the first training set by using the original recognition model to obtain a recognition result;
Calculating a loss value of the real label corresponding to each image in the first training set and the identification result;
and carrying out parameter adjustment on the original recognition model according to the loss value to obtain an initial recognition model.
Optionally, the extracting the first feature vector of the image in the second training set includes:
performing horizontal convolution operation on the images in the second training set by using a preset first gradient operator to obtain horizontal gradient components;
performing vertical convolution operation on the images in the second training set by using a preset second gradient operator to obtain vertical gradient components;
the first feature vector is calculated from the horizontal gradient component and the vertical gradient component.
Optionally, the performing a horizontal convolution operation on the image of the second training set image by using a preset first gradient operator to obtain a horizontal gradient component includes:
acquiring a convolution step length and a convolution length;
calculating the horizontal convolution times according to the convolution step length and the convolution length;
and carrying out convolution operation on each image in the second training set according to the convolution step length by using the first gradient operator, so as to obtain a horizontal gradient component.
Optionally, the calculating the first feature vector according to the horizontal gradient component and the vertical gradient component includes:
carrying out normalization calculation on the horizontal gradient component to obtain a horizontal normalization component;
carrying out normalization calculation on the vertical gradient component to obtain a vertical normalization component;
and square summing the horizontal normalized component and the vertical normalized component to obtain the first feature vector.
In order to solve the above problem, the present invention further provides an apparatus for identifying an object in an image, the apparatus comprising:
the image amplification module is used for acquiring a sample image containing a target object, and carrying out image amplification on the sample image to obtain a sample image set;
the first training module is used for constructing a first training set and a testing set by using the noise images of the same type as the sample images and the sample image set, and training a pre-constructed original recognition model by using the first training set to obtain an initial recognition model;
the model test module is used for testing the initial recognition model by using the test set, selecting a preset type of error result in the test result to construct a second training set, and constructing a third training set by using the sample image;
The feature extraction module is used for extracting a first feature vector of the image in the second training set and extracting a second feature vector of the image in the third training set;
the second training module is used for calculating a loss value between the first feature vector and the second feature vector, and carrying out parameter updating on the initial recognition model according to the loss value to obtain a standard recognition model;
and the image recognition module is used for acquiring an image to be recognized, and carrying out object recognition on the image to be recognized by utilizing the standard recognition model to obtain a target object recognition result in the image.
In order to solve the above-mentioned problems, the present invention also provides an electronic apparatus including:
a memory storing at least one instruction; a kind of electronic device with high-pressure air-conditioning system
And the processor executes the instructions stored in the memory to realize the method for identifying the target in the image.
In order to solve the above-mentioned problems, the present invention also provides a computer-readable storage medium having stored therein at least one instruction that is executed by a processor in an electronic device to implement the above-mentioned method for identifying an object in an image.
According to the embodiment of the invention, the expansion of a small amount of original samples can be realized through image expansion, and the model is trained by utilizing the sample image set and the noise image obtained after expansion, so that the accuracy and the robustness of the model are improved; and then testing the model, constructing a training set by using an error result in the test result, and retraining the model with the sample image to further improve the accuracy of identifying the target object by the model. Therefore, the method, the device, the electronic equipment and the computer readable storage medium for identifying the target in the image can solve the problem of low accuracy in identifying the target.
Drawings
FIG. 1 is a flowchart of a method for identifying an object in an image according to an embodiment of the present invention;
FIG. 2 is a flowchart illustrating a first feature vector generation process according to an embodiment of the present invention;
FIG. 3 is a functional block diagram of an apparatus for identifying an object in an image according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of an electronic device for implementing the method for identifying an object in an image according to an embodiment of the present invention.
The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
The embodiment of the application provides a method for identifying an object in an image. The execution subject of the method for identifying the object in the image includes, but is not limited to, at least one of a server, a terminal, and the like, which can be configured to execute the method provided by the embodiment of the application. In other words, the method for identifying the object in the image may be performed by software or hardware installed in the terminal device or the server device, and the software may be a blockchain platform. The service end includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, and the like.
Referring to fig. 1, a flow chart of a method for identifying an object in an image according to an embodiment of the application is shown. In this embodiment, the method for identifying an object in an image includes:
s1, acquiring a sample image containing a target object, and performing image amplification on the sample image to obtain a sample image set.
In the embodiment of the present application, the sample image includes a specific target object and a real label corresponding to the target object, for example, when the target object is an apple, the sample image is an image including an apple, and the real label is an "apple"; or when the target object is a disease focus, the sample image is an image containing the focus, and the real label is the disease name corresponding to the focus.
According to the embodiment of the invention, the pre-stored sample image can be captured from the pre-constructed blockchain node through the java sentence with the data capturing function, and the efficiency of acquiring the sample image can be improved by utilizing the high throughput of the blockchain on the data.
The embodiment of the invention can realize image amplification of the sample image by performing geometric transformation, color change, contrast adjustment, partial shielding and the like on the sample image.
In one embodiment of the present invention, the sample image is stretched in horizontal and vertical directions with different sizes to obtain a sample image set with the length, width or the combination of the two being transformed.
Or, dyeing the sample image to change the color of the sample image into a plurality of different colors, so as to obtain a sample image set containing the sample images with the plurality of different colors.
Alternatively, a plurality of sets of masked different portions of the sample image are acquired by locally masking the sample image. For example, the upper half of the object in the sample image set is masked to obtain a sample image in which only the lower half of the object is visible, the right half of the object in the sample image set is masked to obtain a sample image in which only the left half of the object is visible, and the sample images in which different areas are masked are collected into a sample image set.
In one embodiment of the present invention, the performing image amplification on the sample image to obtain a sample image set includes:
performing texture drawing on the sample image to obtain an image texture of the sample image;
carrying out random local deepening on the image texture to obtain a texture deepened image;
randomly and locally desalting the image file to obtain a texture desalted image;
and collecting the texture deepening image and the texture fading image into the sample image set.
In detail, the sample image may be textured by using an image texture extraction algorithm such as a GLCM (Gray-level co-occurrence matrix) method, an LBP (Local Binary Pattern ) method, or the like, so as to highlight the image texture in the sample image.
Specifically, the performing random local deepening on the image texture to obtain a texture deepened image includes:
counting the number of textures of the image textures;
selecting image textures with preset proportions as textures to be processed according to the number of the textures;
and adjusting pixel values of the pixels on the texture to be processed to obtain a texture deepened image.
In one application scenario of the invention, the image textures of the preset proportion are selected according to the number of the textures, and the pixel values on the selected textures to be processed are adjusted (for example, the pixel values on the textures to be processed are adjusted to be black ranges) so as to deepen the textures to be processed, and a texture deepen image is obtained.
And similarly, carrying out random local desalination on the image file to obtain a texture desalination image, wherein the step is consistent with the step of deepening the texture, for example, the pixel value on the texture to be processed is adjusted to be in a white range so as to realize the desalination of the texture to be processed and obtain the texture desalination image.
S2, constructing a first training set and a test set by using the noise image of the same type as the sample image and the sample image set, and training a pre-constructed original recognition model by using the first training set to obtain an initial recognition model.
In the embodiment of the present invention, the noise image is an image of the same type as the sample image, but the noise image is different from the target object in the sample image (for example, the noise image and the sample image are both fruit images, but the noise image includes a watermelon, the sample image includes an apple), and similarly, the noise image also includes a real tag corresponding to the noise image. For example, the object contained in the sample image is a lesion of a lung disease, but the noise image contains a lesion of a possible liver disease.
According to the embodiment of the invention, the noise image and the sample image set are collected, and the collected sample image and noise image are divided into a first training set and a test set according to a preset dividing proportion. Wherein the first training set and the test set both comprise a sample image and a noise image.
In the embodiment of the invention, the original recognition model can adopt networks with image recognition functions such as VGG Network, googleNet, residual Network and the like.
In one embodiment of the invention, the afflicientnet is used as the backbone network of the original recognition model, and the afflicientnet is a composite multidimensional convolutional neural network, which is beneficial to improving the accuracy in the image processing process and further improving the accuracy of image recognition.
Further, the embodiment of the invention trains the original recognition model by utilizing the training set so as to realize the adjustment of model parameters in the original recognition model, improve the accuracy of the original recognition model in recognizing the image and obtain the original recognition model.
In detail, training the pre-constructed original recognition model by using the first training set to obtain an initial recognition model, including:
performing image recognition on the first training set by using the original recognition model to obtain a recognition result;
calculating a loss value of the real label corresponding to each image in the first training set and the identification result;
and carrying out parameter adjustment on the original recognition model according to the loss value to obtain an initial recognition model.
Specifically, the recognition result is a recognition result of the original recognition model on the type of the target object contained in each image in the first training set, for example, the first training set includes a sample image a, a sample image B and a noise image C, wherein the real labels of the sample image a and the sample image B are apples (i.e., the target object is an apple), the real labels of the image C are watermelons, and the recognition result obtained by the original recognition model is: the sample image A is apple, the sample image B is watermelon, and the noise image C is apple.
The loss value of the real label corresponding to each image in the first training set and the identification result can be calculated by using a preset first loss function, and then the original identification model is subjected to parameter adjustment according to the loss value, so that the accuracy of the original identification model is improved.
For example, performing vector conversion on the real labels in the first training set to obtain real vectors; vector conversion is carried out on the recognition result to obtain recognition vectors, loss values between the real vectors corresponding to each image in the first training set and the recognition vectors are calculated respectively, and then parameters of an original recognition model are adjusted according to the loss values by using a preset optimization algorithm, wherein the optimization algorithm comprises but is not limited to: a batch gradient descent algorithm, a random gradient descent algorithm, and a small batch gradient descent algorithm.
S3, testing the initial recognition model by using the test set, selecting a preset type of error result in the test result to construct a second training set, and constructing a third training set by using the sample image.
In the embodiment of the present invention, the initial recognition model obtained in step S2 may be tested by using a test set, for example, the test set is input into the initial recognition model, so as to obtain a test result of the initial recognition model on each image in the test set.
In one embodiment of the present invention, when the second training set is constructed by using a preset type of error result in the test result, the preset type of error result includes an error result of a noise image in the test set.
For example, the test set includes a sample image D, a sample image E, a noise image F, and a noise image G, wherein the real labels of the sample image D and the sample image E are apples, and the real labels of the noise image E and the noise image F are watermelons; after the initial recognition model recognizes the test set, the obtained test result is: the sample image D and the noise image F are apples, the sample image E and the noise image G are watermelons, so that the noise image F and the sample image E are error results in the test results, and the noise image F is selected as the second training set.
Further, the embodiment of the invention takes at least one sample image in the sample image set generated in the step S1 as a third training set.
According to the embodiment of the invention, the second training set is constructed by utilizing the error result in the test result, and the third training set is constructed by utilizing the sample image, so that the images which are easy to be identified by the initial identification model in the noise image and the sample image containing the target object can be respectively constructed into the training sets, the subsequent further training of the initial identification model is facilitated, and the accuracy of the initial identification model is improved.
S4, extracting a first feature vector of the image in the second training set, and extracting a second feature vector of the image in the third training set.
In the embodiment of the invention, the first feature vector of the image in the second training set can be extracted by using an algorithm with image feature extraction such as an HOG algorithm, an LBP algorithm, a Harr algorithm and the like.
Further, the step of extracting the second feature vector of the image in the third training set is consistent with the step of extracting the first feature vector of the image in the second training set, which is not described herein.
In one embodiment of the present invention, referring to fig. 2, the extracting the first feature vector of the image in the second training set includes:
S21, performing horizontal convolution operation on the images in the second training set by using a preset first gradient operator to obtain horizontal gradient components;
s22, performing vertical convolution operation on the images in the second training set by using a preset second gradient operator to obtain vertical gradient components;
s23, calculating the first feature vector according to the horizontal gradient component and the vertical gradient component.
In detail, the first gradient operator and the second gradient operator are preset matrixes, for example, the first gradient operator may be [ -1,0,1], the second gradient operator may be [1,0, -1], and the horizontal gradient component and the vertical gradient component corresponding to each image can be obtained by performing convolution operation on the first gradient operator and the second gradient operator and each image in the second training set respectively.
Specifically, the performing a horizontal convolution operation on the image of the second training set image by using a preset first gradient operator to obtain a horizontal gradient component includes:
acquiring a convolution step length and a convolution length;
calculating the horizontal convolution times according to the convolution step length and the convolution length;
and carrying out convolution operation on each image in the second training set according to the convolution step length by using the first gradient operator, so as to obtain a horizontal gradient component.
The convolution step length refers to the pixel length of each image in the second training set, which needs to be moved after the first gradient operator performs one convolution operation, and the convolution length refers to the pixel length of each image in the horizontal direction, and the convolution step length is divided by the convolution length, so that the number of horizontal convolutions of each image in the second training set, which needs to be performed in the horizontal direction, can be calculated.
In one embodiment of the present invention, the calculating the first feature vector according to the horizontal gradient component and the vertical gradient component includes:
carrying out normalization calculation on the horizontal gradient component to obtain a horizontal normalization component;
carrying out normalization calculation on the vertical gradient component to obtain a vertical normalization component;
and square summing the horizontal normalized component and the vertical normalized component to obtain the first feature vector.
In detail, the horizontal gradient component and the vertical gradient component can be calculated by adopting a function with the threshold value range of (0, 1) such as a preset linear function, a logarithmic function or an anti-cotangent function, so as to realize the normalization processing of the horizontal gradient component and the vertical gradient component.
Specifically, the horizontal normalized component and the vertical normalized component may be squared and summed using the following squaring and summing formula to obtain the first feature vector:
Wherein L is the first eigenvector, α is the horizontal normalized component, and β is the vertical normalized component.
S5, calculating a loss value between the first feature vector and the second feature vector, and carrying out parameter updating on the initial recognition model according to the loss value to obtain a standard recognition model.
In the embodiment of the invention, the loss value of the real label corresponding to each image in the first training set and the identification result can be calculated by using the preset second loss function, so that the parameter adjustment of the initial identification model is realized, and the accuracy of the initial identification model is improved.
The second loss function may be the same as or different from the first loss function in step S2.
In detail, the step of updating parameters of the initial recognition model by using the loss value to obtain a standard recognition model is consistent with the step of adjusting parameters of the original recognition model when the pre-constructed original recognition model is trained by using the first training set in the step S2, and is not described herein.
S6, acquiring an image to be identified, and carrying out object identification on the image to be identified by utilizing the standard identification model to obtain an object identification result in the image.
In the embodiment of the invention, the image to be identified can be an image containing the target object or not, and when the image button to be identified is obtained, the standard identification model can be utilized to identify the target object of the image to be identified, so as to obtain the identification result of the type of the target object in the image.
According to the embodiment of the invention, the expansion of a small amount of original samples can be realized through image expansion, and the model is trained by utilizing the sample image set and the noise image obtained after expansion, so that the accuracy and the robustness of the model are improved; and then testing the model, constructing a training set by using an error result in the test result, and retraining the model with the sample image to further improve the accuracy of identifying the target object by the model. Therefore, the method for identifying the target in the image can solve the problem of low accuracy in identifying the target.
Fig. 3 is a functional block diagram of an apparatus for identifying an object in an image according to an embodiment of the present invention.
The object recognition device 100 in an image according to the present invention may be installed in an electronic apparatus. The device for identifying an object in an image 100 may include an image amplification module 101, a first training module 102, a model test module 103, a feature extraction module 104, a second training module 105, and an image identification module 106 according to the implemented functions. The module of the invention, which may also be referred to as a unit, refers to a series of computer program segments, which are stored in the memory of the electronic device, capable of being executed by the processor of the electronic device and of performing a fixed function.
In the present embodiment, the functions concerning the respective modules/units are as follows:
the image amplification module 101 is configured to obtain a sample image containing a target object, and perform image amplification on the sample image to obtain a sample image set;
in the embodiment of the present invention, the sample image includes a specific target object and a real label corresponding to the target object, for example, when the target object is an apple, the sample image is an image including an apple, and the real label is an "apple"; or when the target object is a disease focus, the sample image is an image containing the focus, and the real label is the disease name corresponding to the focus.
According to the embodiment of the invention, the pre-stored sample image can be captured from the pre-constructed blockchain node through the java sentence with the data capturing function, and the efficiency of acquiring the sample image can be improved by utilizing the high throughput of the blockchain on the data.
The embodiment of the invention can realize image amplification of the sample image by performing geometric transformation, color change, contrast adjustment, partial shielding and the like on the sample image.
In one embodiment of the present invention, the sample image is stretched in horizontal and vertical directions with different sizes to obtain a sample image set with the length, width or the combination of the two being transformed.
Or, dyeing the sample image to change the color of the sample image into a plurality of different colors, so as to obtain a sample image set containing the sample images with the plurality of different colors.
Alternatively, a plurality of sets of masked different portions of the sample image are acquired by locally masking the sample image. For example, the upper half of the object in the sample image set is masked to obtain a sample image in which only the lower half of the object is visible, the right half of the object in the sample image set is masked to obtain a sample image in which only the left half of the object is visible, and the sample images in which different areas are masked are collected into a sample image set.
In one embodiment of the present invention, the image amplification module 101 is specifically configured to:
acquiring a sample image containing a target object;
performing texture drawing on the sample image to obtain an image texture of the sample image;
carrying out random local deepening on the image texture to obtain a texture deepened image;
randomly and locally desalting the image file to obtain a texture desalted image;
and collecting the texture deepening image and the texture fading image into the sample image set.
In detail, the sample image may be textured by using an image texture extraction algorithm such as a GLCM (Gray-level co-occurrence matrix) method, an LBP (Local Binary Pattern ) method, or the like, so as to highlight the image texture in the sample image.
Specifically, the performing random local deepening on the image texture to obtain a texture deepened image includes:
counting the number of textures of the image textures;
selecting image textures with preset proportions as textures to be processed according to the number of the textures;
and adjusting pixel values of the pixels on the texture to be processed to obtain a texture deepened image.
In one application scenario of the invention, the image textures of the preset proportion are selected according to the number of the textures, and the pixel values on the selected textures to be processed are adjusted (for example, the pixel values on the textures to be processed are adjusted to be black ranges) so as to deepen the textures to be processed, and a texture deepen image is obtained.
And similarly, carrying out random local desalination on the image file to obtain a texture desalination image, wherein the step is consistent with the step of deepening the texture, for example, the pixel value on the texture to be processed is adjusted to be in a white range so as to realize the desalination of the texture to be processed and obtain the texture desalination image.
The first training module 102 is configured to construct a first training set and a test set by using a noise image of the same type as the sample image and the sample image set, and train a pre-constructed original recognition model by using the first training set to obtain an initial recognition model;
in the embodiment of the present invention, the noise image is an image of the same type as the sample image, but the noise image is different from the target object in the sample image (for example, the noise image and the sample image are both fruit images, but the noise image includes a watermelon, the sample image includes an apple), and similarly, the noise image also includes a real tag corresponding to the noise image. For example, the object contained in the sample image is a lesion of a lung disease, but the noise image contains a lesion of a possible liver disease.
According to the embodiment of the invention, the noise image and the sample image set are collected, and the collected sample image and noise image are divided into a first training set and a test set according to a preset dividing proportion. Wherein the first training set and the test set both comprise a sample image and a noise image.
In the embodiment of the invention, the original recognition model can adopt networks with image recognition functions such as VGG Network, googleNet, residual Network and the like.
In one embodiment of the invention, the afflicientnet is used as the backbone network of the original recognition model, and the afflicientnet is a composite multidimensional convolutional neural network, which is beneficial to improving the accuracy in the image processing process and further improving the accuracy of image recognition.
Further, the embodiment of the invention trains the original recognition model by utilizing the training set so as to realize the adjustment of model parameters in the original recognition model, improve the accuracy of the original recognition model in recognizing the image and obtain the original recognition model.
In detail, the first training module 102 is specifically configured to:
performing image recognition on the first training set by using the original recognition model to obtain a recognition result;
calculating a loss value of the real label corresponding to each image in the first training set and the identification result;
and carrying out parameter adjustment on the original recognition model according to the loss value to obtain an initial recognition model.
Specifically, the recognition result is a recognition result of the original recognition model on the type of the target object contained in each image in the first training set, for example, the first training set includes a sample image a, a sample image B and a noise image C, wherein the real labels of the sample image a and the sample image B are apples (i.e., the target object is an apple), the real labels of the image C are watermelons, and the recognition result obtained by the original recognition model is: the sample image A is apple, the sample image B is watermelon, and the noise image C is apple.
The loss value of the real label corresponding to each image in the first training set and the identification result can be calculated by using a preset first loss function, and then the original identification model is subjected to parameter adjustment according to the loss value, so that the accuracy of the original identification model is improved.
For example, performing vector conversion on the real labels in the first training set to obtain real vectors; vector conversion is carried out on the recognition result to obtain recognition vectors, loss values between the real vectors corresponding to each image in the first training set and the recognition vectors are calculated respectively, and then parameters of an original recognition model are adjusted according to the loss values by using a preset optimization algorithm, wherein the optimization algorithm comprises but is not limited to: a batch gradient descent algorithm, a random gradient descent algorithm, and a small batch gradient descent algorithm.
The model test module 103 is configured to test the initial recognition model by using the test set, select a preset type of error result in the test result, construct a second training set, and construct a third training set by using the sample image;
in the embodiment of the present invention, the initial recognition model obtained in the first training module 102 may be tested by using a test set, for example, the test set is input into the initial recognition model, so as to obtain a test result of the initial recognition model on each image in the test set.
In one embodiment of the present invention, when the second training set is constructed by using a preset type of error result in the test result, the preset type of error result includes an error result of a noise image in the test set.
For example, the test set includes a sample image D, a sample image E, a noise image F, and a noise image G, wherein the real labels of the sample image D and the sample image E are apples, and the real labels of the noise image E and the noise image F are watermelons; after the initial recognition model recognizes the test set, the obtained test result is: the sample image D and the noise image F are apples, the sample image E and the noise image G are watermelons, so that the noise image F and the sample image E are error results in the test results, and the noise image F is selected as the second training set.
Further, the embodiment of the present invention uses at least one sample image in the sample image set generated in the image amplification module 101 as the third training set.
According to the embodiment of the invention, the second training set is constructed by utilizing the error result in the test result, and the third training set is constructed by utilizing the sample image, so that the images which are easy to be identified by the initial identification model in the noise image and the sample image containing the target object can be respectively constructed into the training sets, the subsequent further training of the initial identification model is facilitated, and the accuracy of the initial identification model is improved.
The feature extraction module 104 is configured to extract a first feature vector of the image in the second training set, and extract a second feature vector of the image in the third training set;
in the embodiment of the invention, the first feature vector of the image in the second training set can be extracted by using an algorithm with image feature extraction such as an HOG algorithm, an LBP algorithm, a Harr algorithm and the like.
Further, the step of extracting the second feature vector of the image in the third training set is consistent with the step of extracting the first feature vector of the image in the second training set, which is not described herein.
In one embodiment of the present invention, the feature extraction module 104 is specifically configured to:
performing horizontal convolution operation on the images in the second training set by using a preset first gradient operator to obtain horizontal gradient components;
performing vertical convolution operation on the images in the second training set by using a preset second gradient operator to obtain vertical gradient components;
calculating the first eigenvector from the horizontal gradient component and the vertical gradient component;
and extracting a second feature vector of the image in the third training set.
In detail, the first gradient operator and the second gradient operator are preset matrixes, for example, the first gradient operator may be [ -1,0,1], the second gradient operator may be [1,0, -1], and the horizontal gradient component and the vertical gradient component corresponding to each image can be obtained by performing convolution operation on the first gradient operator and the second gradient operator and each image in the second training set respectively.
Specifically, the performing a horizontal convolution operation on the image of the second training set image by using a preset first gradient operator to obtain a horizontal gradient component includes:
acquiring a convolution step length and a convolution length;
calculating the horizontal convolution times according to the convolution step length and the convolution length;
and carrying out convolution operation on each image in the second training set according to the convolution step length by using the first gradient operator, so as to obtain a horizontal gradient component.
The convolution step length refers to the pixel length of each image in the second training set, which needs to be moved after the first gradient operator performs one convolution operation, and the convolution length refers to the pixel length of each image in the horizontal direction, and the convolution step length is divided by the convolution length, so that the number of horizontal convolutions of each image in the second training set, which needs to be performed in the horizontal direction, can be calculated.
In one embodiment of the present invention, the calculating the first feature vector according to the horizontal gradient component and the vertical gradient component includes:
carrying out normalization calculation on the horizontal gradient component to obtain a horizontal normalization component;
carrying out normalization calculation on the vertical gradient component to obtain a vertical normalization component;
And square summing the horizontal normalized component and the vertical normalized component to obtain the first feature vector.
In detail, the horizontal gradient component and the vertical gradient component can be calculated by adopting a function with the threshold value range of (0, 1) such as a preset linear function, a logarithmic function or an anti-cotangent function, so as to realize the normalization processing of the horizontal gradient component and the vertical gradient component.
Specifically, the horizontal normalized component and the vertical normalized component may be squared and summed using the following squaring and summing formula to obtain the first feature vector:
wherein L is the first eigenvector, α is the horizontal normalized component, and β is the vertical normalized component.
The second training module 105 is configured to calculate a loss value between the first feature vector and the second feature vector, and update parameters of the initial recognition model according to the loss value to obtain a standard recognition model;
in the embodiment of the invention, the loss value of the real label corresponding to each image in the first training set and the identification result can be calculated by using the preset second loss function, so that the parameter adjustment of the initial identification model is realized, and the accuracy of the initial identification model is improved.
The second loss function may be the same as or different from the first loss function in the first training module 102.
In detail, the step of updating the parameters of the initial recognition model by using the loss value to obtain the standard recognition model is consistent with the step of adjusting the parameters of the original recognition model when the first training set is used to train the pre-constructed original recognition model in the first training module 102, which is not described herein.
The image recognition module 106 is configured to obtain an image to be recognized, and perform object recognition on the image to be recognized by using the standard recognition model, so as to obtain a recognition result of the object in the image.
In the embodiment of the invention, the image to be identified can be an image containing the target object or not, and when the image button to be identified is obtained, the standard identification model can be utilized to identify the target object of the image to be identified, so as to obtain the identification result of the type of the target object in the image.
According to the embodiment of the invention, the expansion of a small amount of original samples can be realized through image expansion, and the model is trained by utilizing the sample image set and the noise image obtained after expansion, so that the accuracy and the robustness of the model are improved; and then testing the model, constructing a training set by using an error result in the test result, and retraining the model with the sample image to further improve the accuracy of identifying the target object by the model. Therefore, the device for identifying the target in the image can solve the problem of low accuracy in identifying the target.
Fig. 4 is a schematic structural diagram of an electronic device for implementing a method for identifying an object in an image according to an embodiment of the present invention.
The electronic device 1 may comprise a processor 10, a memory 11 and a bus, and may further comprise a computer program, such as an in-image object recognition program 12, stored in the memory 11 and executable on the processor 10.
The memory 11 includes at least one type of readable storage medium, including flash memory, a mobile hard disk, a multimedia card, a card memory (e.g., SD or DX memory, etc.), a magnetic memory, a magnetic disk, an optical disk, etc. The memory 11 may in some embodiments be an internal storage unit of the electronic device 1, such as a removable hard disk of the electronic device 1. The memory 11 may in other embodiments also be an external storage device of the electronic device 1, such as a plug-in mobile hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) or the like, which are provided on the electronic device 1. Further, the memory 11 may also include both an internal storage unit and an external storage device of the electronic device 1. The memory 11 may be used not only for storing application software installed in the electronic device 1 and various types of data, such as codes of the object recognition program 12 in an image, but also for temporarily storing data that has been output or is to be output.
The processor 10 may be comprised of integrated circuits in some embodiments, for example, a single packaged integrated circuit, or may be comprised of multiple integrated circuits packaged with the same or different functions, including one or more central processing units (Central Processing unit, CPU), microprocessors, digital processing chips, graphics processors, combinations of various control chips, and the like. The processor 10 is a Control Unit (Control Unit) of the electronic device, connects respective parts of the entire electronic device using various interfaces and lines, executes or executes programs or modules (for example, an in-image object recognition program or the like) stored in the memory 11, and invokes data stored in the memory 11 to perform various functions of the electronic device 1 and process data.
The bus may be a peripheral component interconnect standard (peripheral component interconnect, PCI) bus or an extended industry standard architecture (extended industry standard architecture, EISA) bus, among others. The bus may be classified as an address bus, a data bus, a control bus, etc. The bus is arranged to enable a connection communication between the memory 11 and at least one processor 10 etc.
Fig. 4 shows only an electronic device with components, it being understood by a person skilled in the art that the structure shown in fig. 4 does not constitute a limitation of the electronic device 1, and may comprise fewer or more components than shown, or may combine certain components, or may be arranged in different components.
For example, although not shown, the electronic device 1 may further include a power source (such as a battery) for supplying power to each component, and preferably, the power source may be logically connected to the at least one processor 10 through a power management device, so that functions of charge management, discharge management, power consumption management, and the like are implemented through the power management device. The power supply may also include one or more of any of a direct current or alternating current power supply, recharging device, power failure detection circuit, power converter or inverter, power status indicator, etc. The electronic device 1 may further include various sensors, bluetooth modules, wi-Fi modules, etc., which will not be described herein.
Further, the electronic device 1 may also comprise a network interface, optionally the network interface may comprise a wired interface and/or a wireless interface (e.g. WI-FI interface, bluetooth interface, etc.), typically used for establishing a communication connection between the electronic device 1 and other electronic devices.
The electronic device 1 may optionally further comprise a user interface, which may be a Display, an input unit, such as a Keyboard (Keyboard), or a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch, or the like. The display may also be referred to as a display screen or display unit, as appropriate, for displaying information processed in the electronic device 1 and for displaying a visual user interface.
It should be understood that the embodiments described are for illustrative purposes only and are not limited to this configuration in the scope of the patent application.
The object recognition program 12 in the image stored in the memory 11 of the electronic device 1 is a combination of a plurality of instructions, which when executed in the processor 10, can implement:
acquiring a sample image containing a target object, and performing image amplification on the sample image to obtain a sample image set;
constructing a first training set and a test set by using a noise image of the same type as the sample image and the sample image set, and training a pre-constructed original recognition model by using the first training set to obtain an initial recognition model;
Testing the initial recognition model by using the test set, selecting a preset type of error result in the test result to construct a second training set, and constructing a third training set by using the sample image;
extracting a first feature vector of the image in the second training set, and extracting a second feature vector of the image in the third training set;
calculating a loss value between the first feature vector and the second feature vector, and carrying out parameter updating on the initial recognition model according to the loss value to obtain a standard recognition model;
and acquiring an image to be identified, and identifying a target object in the image to be identified by utilizing the standard identification model to obtain a target object identification result in the image.
Specifically, the specific implementation method of the above instruction by the processor 10 may refer to descriptions of related steps in the corresponding embodiments of fig. 1 to 4, which are not repeated herein.
Further, the modules/units integrated in the electronic device 1 may be stored in a computer readable storage medium if implemented in the form of software functional units and sold or used as separate products. The computer readable storage medium may be volatile or nonvolatile. For example, the computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM).
The present invention also provides a computer readable storage medium storing a computer program which, when executed by a processor of an electronic device, can implement:
acquiring a sample image containing a target object, and performing image amplification on the sample image to obtain a sample image set;
constructing a first training set and a test set by using a noise image of the same type as the sample image and the sample image set, and training a pre-constructed original recognition model by using the first training set to obtain an initial recognition model;
testing the initial recognition model by using the test set, selecting a preset type of error result in the test result to construct a second training set, and constructing a third training set by using the sample image;
extracting a first feature vector of the image in the second training set, and extracting a second feature vector of the image in the third training set;
calculating a loss value between the first feature vector and the second feature vector, and carrying out parameter updating on the initial recognition model according to the loss value to obtain a standard recognition model;
and acquiring an image to be identified, and identifying a target object in the image to be identified by utilizing the standard identification model to obtain a target object identification result in the image.
In the several embodiments provided in the present invention, it should be understood that the disclosed apparatus, device and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is merely a logical function division, and there may be other manners of division when actually implemented.
The modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical units, may be located in one place, or may be distributed over multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional module in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units can be realized in a form of hardware or a form of hardware and a form of software functional modules.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof.
The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.
The blockchain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, consensus mechanism, encryption algorithm and the like. The Blockchain (Blockchain), which is essentially a decentralised database, is a string of data blocks that are generated by cryptographic means in association, each data block containing a batch of information of network transactions for verifying the validity of the information (anti-counterfeiting) and generating the next block. The blockchain may include a blockchain underlying platform, a platform product services layer, an application services layer, and the like.
Furthermore, it is evident that the word "comprising" does not exclude other elements or steps, and that the singular does not exclude a plurality. A plurality of units or means recited in the system claims can also be implemented by means of software or hardware by means of one unit or means. The terms second, etc. are used to denote a name, but not any particular order.
Finally, it should be noted that the above-mentioned embodiments are merely for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made to the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention.
Claims (6)
1. A method for identifying an object in an image, the method comprising:
acquiring a sample image containing a target object, and performing image amplification on the sample image to obtain a sample image set;
constructing a first training set and a test set by using a noise image of the same type as the sample image and the sample image set, and training a pre-constructed original recognition model by using the first training set to obtain an initial recognition model;
testing the initial recognition model by using the test set, selecting a preset type of error result in the test result to construct a second training set, and constructing a third training set by using the sample image;
extracting a first feature vector of the image in the second training set, and extracting a second feature vector of the image in the third training set;
Calculating a loss value between the first feature vector and the second feature vector, and carrying out parameter updating on the initial recognition model according to the loss value to obtain a standard recognition model;
acquiring an image to be identified, and identifying a target object in the image to be identified by utilizing the standard identification model to obtain a target object identification result in the image;
the image amplification is performed on the sample image to obtain a sample image set, which comprises the following steps: performing texture drawing on the sample image to obtain an image texture of the sample image; carrying out random local deepening on the image texture to obtain a texture deepened image; randomly and locally desalting the image textures to obtain a texture desalted image; collecting the texture deepened image and the texture faded image into the sample image set;
the extracting the first feature vector of the image in the second training set includes: performing horizontal convolution operation on the images in the second training set by using a preset first gradient operator to obtain horizontal gradient components; performing vertical convolution operation on the images in the second training set by using a preset second gradient operator to obtain vertical gradient components; calculating the first eigenvector from the horizontal gradient component and the vertical gradient component;
The step of performing horizontal convolution operation on the images in the second training set by using a preset first gradient operator to obtain horizontal gradient components comprises the following steps: acquiring a convolution step length and a convolution length; calculating the horizontal convolution times according to the convolution step length and the convolution length; carrying out convolution operation on each image in the second training set according to the convolution step length by utilizing the first gradient operator, so as to obtain a horizontal gradient component;
the computing the first feature vector from the horizontal gradient component and the vertical gradient component includes: carrying out normalization calculation on the horizontal gradient component to obtain a horizontal normalization component; carrying out normalization calculation on the vertical gradient component to obtain a vertical normalization component; and square summing the horizontal normalized component and the vertical normalized component to obtain the first feature vector.
2. The method for identifying an object in an image according to claim 1, wherein said randomly locally deepening the texture of the image to obtain a texture deepened image comprises:
counting the number of textures of the image textures;
selecting image textures with preset proportions as textures to be processed according to the number of the textures;
And adjusting pixel values of the pixels on the texture to be processed to obtain a texture deepened image.
3. The method for identifying an object in an image according to claim 1, wherein training a pre-constructed original identification model by using the first training set to obtain an initial identification model comprises:
performing image recognition on the first training set by using the original recognition model to obtain a recognition result;
calculating a loss value of the real label corresponding to each image in the first training set and the identification result;
and carrying out parameter adjustment on the original recognition model according to the loss value to obtain an initial recognition model.
4. An in-image object recognition apparatus for realizing the in-image object recognition method according to any one of claims 1 to 3, characterized in that the apparatus comprises:
the image amplification module is used for acquiring a sample image containing a target object, and carrying out image amplification on the sample image to obtain a sample image set;
the first training module is used for constructing a first training set and a testing set by using the noise images of the same type as the sample images and the sample image set, and training a pre-constructed original recognition model by using the first training set to obtain an initial recognition model;
The model test module is used for testing the initial recognition model by using the test set, selecting a preset type of error result in the test result to construct a second training set, and constructing a third training set by using the sample image;
the feature extraction module is used for extracting a first feature vector of the image in the second training set and extracting a second feature vector of the image in the third training set;
the second training module is used for calculating a loss value between the first feature vector and the second feature vector, and carrying out parameter updating on the initial recognition model according to the loss value to obtain a standard recognition model;
and the image recognition module is used for acquiring an image to be recognized, and carrying out object recognition on the image to be recognized by utilizing the standard recognition model to obtain a target object recognition result in the image.
5. An electronic device, the electronic device comprising:
at least one processor; the method comprises the steps of,
a memory communicatively coupled to the at least one processor; wherein, the liquid crystal display device comprises a liquid crystal display device,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of object identification in an image as claimed in any one of claims 1 to 3.
6. A computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the method for identifying an object in an image according to any one of claims 1 to 3.
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| CN115546536A (en) * | 2022-09-22 | 2022-12-30 | 南京森林警察学院 | Ivory product identification method and system |
| CN117094966B (en) * | 2023-08-21 | 2024-04-05 | 青岛美迪康数字工程有限公司 | Tongue image identification method and device based on image amplification and computer equipment |
| CN116958503B (en) * | 2023-09-19 | 2024-03-12 | 广东新泰隆环保集团有限公司 | Image processing-based sludge drying grade identification method and system |
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