WO2020173177A1 - 物体色差缺陷检测方法、装置、计算机设备及存储介质 - Google Patents
物体色差缺陷检测方法、装置、计算机设备及存储介质 Download PDFInfo
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Definitions
- This application relates to an object color difference defect detection method, device, computer equipment and storage medium.
- the traditional surface chromatic aberration detection method is to arrange professional quality inspectors to use manual visual inspection to detect the surface chromatic aberration defects of the object to be tested.
- a method, device, computer equipment, and storage medium for detecting object color difference defects are provided.
- An object color difference defect detection method including:
- the object color defect detection result of the object to be tested is obtained.
- An object color difference defect detection device including:
- Multiple image acquisition module for acquiring multiple color images of the object to be measured
- Single-channel image acquisition module used to perform principal component analysis on multiple color images to acquire single-channel images of the object to be measured
- the gray value extraction module is used to extract the gray value of pixels in a single-channel image
- the pixel defect result acquisition module is used to input the pixel gray value of the single-channel image into the BP neural network model based on the gradient descent algorithm to obtain the pixel defect detection result;
- the color difference defect result acquisition module is used to obtain the object color difference defect detection result of the object to be tested according to the pixel defect detection result.
- a computer device including a memory and one or more processors, the memory stores computer readable instructions, when the computer readable instructions are executed by the processor, the one or more processors execute The following steps:
- the object color defect detection result of the object to be tested is obtained.
- One or more non-volatile computer-readable storage media storing computer-readable instructions.
- the one or more processors execute the following steps:
- the object color defect detection result of the object to be tested is obtained.
- FIG. 1 is a schematic flowchart of a method for detecting color difference defects of an object according to one or more embodiments.
- Fig. 2 is a schematic flowchart of a method for detecting color difference defects of an object in another embodiment.
- Fig. 3 is a block diagram of an object color difference defect detection device according to one or more embodiments.
- Figure 4 is a block diagram of a computer device according to one or more embodiments.
- a method for detecting object color difference defects which includes the following steps:
- S110 Acquire multiple color images of the object to be measured.
- Multivariate color image is a measurement that represents multiple different properties of the same object, and corresponds to the image data of multiple channels in space, that is, the image of multiple channels is superimposed on the gray value, and the pixel coordinates of the single-channel image are compared with the two-dimensional
- the multi-element image adds one-dimensional scale information to the two-dimensional basis, making it a three-dimensional color image.
- the scale variable can be represented by any parameter. Taking the stainless steel steel plate as an example, the multi-color image of the stainless steel plate is acquired.
- S120 Perform principal component analysis on the multivariate color image to obtain a single-channel image of the object to be measured.
- an image includes a pixel map composed of pixels, which is an image formed by a regular and clever combination and arrangement of a single pixel as a unit.
- the image channel refers to the separate red R, green G, and blue B parts.
- the image is generally composed of three channels of red, green and blue.
- the pixels of the image are all There are three values; but the image is not limited to three-channel images, it can be three-channel images or four-channel images and other multi-channel images.
- CMYK mode has 4 channels, and the pixels of a 4-channel image have 4 values.
- the single-channel image means that each pixel has only one value to represent the color, which can be understood as a grayscale image.
- the multi-color image is a multi-channel three-dimensional color image, and the principal component analysis is performed on the multi-color image to obtain a single-channel image that meets the requirements.
- the number of single-channel images obtained can be multiple, which is understandable Yes, this embodiment does not require the number of single-channel images.
- Gray value refers to the fact that because the color and brightness of each point in the scene are different, each point on the black-and-white photo or the black-and-white image reproduced by the TV shows different degrees of gray.
- the logarithmic relationship between white and black is divided into several Level, called gray level. The range is generally from 0 to 225.
- the gray value in the single-channel image can be extracted by any of the floating-point algorithm, integer method, shift method, or averaging method.
- S140 Input the pixel gray value of the single-channel image into the BP neural network model constructed based on the gradient descent algorithm to obtain the pixel defect detection result.
- the BP neural network model is a multilayer feedforward neural network trained according to the error back propagation algorithm.
- the main idea is to propagate the error of the output layer layer by layer from back to front to indirectly calculate the hidden layer error.
- the input information is calculated from the input layer through the hidden layer layer by layer to calculate the output value of each unit.
- the output error is calculated layer by layer forward to calculate the error between each unit of the hidden layer, and the error is used to correct the weight of the previous layer.
- the gradient descent algorithm is a kind of iterative method, which can solve the least squares problem. Through the step by step iterative solution of the gradient descent algorithm, the minimized loss function and model parameters are obtained.
- the BP neural network model is constructed based on the gradient descent algorithm. After inputting the gray values of the pixels of the single-channel image into the model, the constructed BP neural network model is used to identify whether the pixels are qualified. When the defect is unqualified, the model outputs a failure message. When the pixel has no defect and is qualified, it outputs a pixel qualified message. Specifically, when the pixel is identified as defective and unqualified, it outputs 0, indicating that the pixel is abnormal. When the defect is qualified, 1 is output, indicating that the pixel is normal.
- Pixels are a form of image color.
- the pixels of the image are input to the BP neural network model one by one, the output results of the BP network model are recognized, and the defect recognition results of the pixels in the entire image are obtained.
- the pixels have defects
- the color of the image of the object to be measured composed of pixels is an image with chromatic aberration defects, so there are chromatic aberration defects on the surface of the stainless steel plate.
- the defective pixels are marked as 0, the non-defective pixels are marked as 1, and the mark of the pixel is identified, and the position of the defective pixel is tracked.
- the surface of the stainless steel steel plate has chromatic aberration defects.
- the preset threshold may not be limited in this embodiment, and may be set according to the actual situation of production requirements. If not necessary, when the color difference test result of the stainless steel plate is obtained, record the model of the stainless steel plate and the corresponding color difference defect, calculate the pass rate of the stainless steel plate, and send the test result, record result, pass rate and other information To the management terminal.
- the above-mentioned object chromatic aberration defect detection method Through the principal component analysis of the multivariate color image, the multivariate analysis of the image of the object to be measured can be performed to obtain the single-channel image of the object to be measured, and the pixel gray value of the extracted single-channel image is input into the BP nerve constructed based on the gradient descent algorithm
- the network model automatically compares and detects the pixel chromatic aberration defects of the image of the object to be tested. According to the pixel defect detection result of the image of the object to be tested, the chromatic defect detection result of the object to be tested is obtained. Manual operation is not required, and automatic learning and detection are performed, and the result is accurate.
- the pixel gray value of the single-channel image is input into the BP neural network model constructed based on the gradient descent algorithm, and before the pixel defect detection result is obtained, it also includes: constructing the BP neural network initial model, the BP neural network initial
- the input layer of the model is constructed by the pixel gray value of the single-channel image as the input vector.
- the output layer of the BP neural network initial model is constructed by the pixel defect detection results of each object to be tested as the output vector.
- the hidden layer of the BP neural network model Constructed by the mapping relationship between the input vector and the output vector; according to the pixel gray value of the single-channel image of each object to be tested, the initial model of the BP neural network is trained through the gradient descent algorithm to obtain the BP neural network model.
- the number of neurons in the hidden layer is set to 4.
- the gray value of the pixel is used as the training sample, and the initial model of the BP neural network is trained according to the gray value of the normal pixel to obtain the BP neural network model.
- the BP neural network model is constructed to carry out autonomous learning and realize automatic detection.
- the initial model of the BP neural network is trained through the gradient descent algorithm, and the BP neural network model obtained includes: using the mean square error function mse performs error analysis on the initial model of BP neural network.
- the BP neural network model is obtained.
- the mean square error function mse expressed as the expected value of the square of the difference between the estimated value of the parameter and the true value of the parameter in parameter estimation, is denoted as mse.
- the error analysis is performed, and the preset error is set to 0.10.
- the training error converges to within 0.01, stop Train the initial model of the BP neural network to obtain the BP neural network model.
- the number of training iterations is 1000
- the single training error is greater than 0.01
- the training is also stopped, and the learning rate is 0.01. It can be understood that the number of iterations and the preset error value are not the only settings, and can be selected according to actual conditions.
- the preset error value of 0.01 and the number of iterations of 1000 are obtained through multiple experiments.
- the value is 0.01
- the convergence speed can be accelerated, the error shock can be reduced, and the optimal training result can be guaranteed.
- Perform performance analysis through the mean square error function to reduce the error of the detection result and improve the accuracy of the result.
- S110 includes:
- S101 Collect an image of the object to be measured.
- S102 Perform convolution processing on the image of the object to be measured to obtain a multi-scale image of the object to be measured.
- an industrial camera such as a CCD (Charge Coupled Device) camera or a CMOS (Complementary Metal Oxide Semiconductor) camera is used to take a color image of the surface of the stainless steel plate of the object to be measured.
- Gaussian kernel filter convolution under different scales to obtain a two-dimensional multi-scale image, and then through the cat (Concatenate) function to convert the multi-scale image into a three-dimensional multi-color image
- the cat function is a function in matlab, used to construct Multidimensional Arrays.
- the Gaussian kernel function is defined as a monotonic function of the Euclidean distance from any point X to a certain center point Y in space:
- ⁇ is the radial range of the function, which is called the scale parameter.
- the address pointer is used to point to the first address of R, G, and B of the color image on the surface of stainless steel plate.
- the multi-scale image is obtained by collecting the color image of the stainless steel steel plate and convolving the color image of the stainless steel steel plate.
- the multi-scale image is converted into a multi-color image, and the image can be observed from multiple aspects. Makes the defect detection result more accurate.
- performing principal component analysis on the multivariate color image to obtain a single-channel image of the object to be measured includes: expanding the multivariate color image to obtain the corresponding two-dimensional data matrix; standardizing the two-dimensional data matrix to obtain the agreement Variance matrix; perform eigenvalue decomposition on the covariance matrix to obtain eigenvalues and eigenvectors; perform matrix conversion according to eigenvectors and eigenvalues to obtain feature images, and there are multiple feature images; sequentially calculate the variance contribution rate of the feature images; select cumulative The feature image whose variance contribution rate reaches the preset threshold is regarded as a single-channel image.
- the expanded two-dimensional data matrix that is, the two-dimensional multivariate image matrix X N ⁇ M can be written as:
- the variance contribution rate refers to the variance caused by a single common factor.
- the proportion of the total variation indicates the influence of the common factor on the dependent variable.
- the cumulative variance contribution rate is the proportion of the variation caused by all common factors to the total variation, indicating the total influence of all common factors on the dependent variable, and the contribution rates of the differences are added. Equal to the cumulative variance contribution rate.
- the common factor is the feature image
- the variance contribution rate of each feature image is calculated
- the cumulative variance contribution rate is calculated sequentially from the first feature image.
- the cumulative variance contribution rate reaches 95%
- the cumulative variance rate is selected
- the multiple feature images reaching 95% are treated as multiple single-channel images.
- the preset threshold is not limited to 95%, and a reasonable choice can be made according to the situation.
- This embodiment can select an image with a major influence from a large number of images from multiple observation angles for detection, which improves the detection efficiency while ensuring the accuracy of the result.
- the method further includes: S160: When the area of the color difference defect area formed by the pixel points with the color difference defect in the pixel defect detection result is greater than the preset threshold, perform the color difference defect area mark.
- the image is composed of countless pixel permutations and combinations.
- the BP neural network model when the pixel is defective, the BP neural network model outputs 0, and when the pixel has no defects, the BP neural network model outputs 1.
- the result is that the area of the area formed by the pixel points of 0 is greater than the preset threshold, it is recognized as a defect area, and the defect area is highlighted on the image of the object to be tested.
- the defect position of the object to be tested can be quickly found, the detection efficiency is improved, no manual operation is required, and the accuracy of the result is ensured.
- the method further includes: S170: recording the number of color difference defect areas; and sending a defect warning message according to the number of color difference defect areas.
- the warning message can include warning level information, for example, if the number of defective areas is between 1-2, it is a mild warning, the number of defective areas is between 3-5, it is a medium warning, and the number of defective areas is more than 5 is a severe warning. .
- the warning message may include information such as stainless steel plate model information, inspection time, and inspection machine number. By sending an early warning message, it can prompt the manager to confirm and inspect the defect result, ensuring the accuracy of the automated defect inspection process.
- an object color difference defect detection device which includes the following modules, wherein:
- the multiple image acquisition module 310 is used to acquire multiple color images of the object to be measured
- the single-channel image acquisition module 320 is configured to perform principal component analysis on the multi-color image to acquire a single-channel image of the object to be measured;
- the gray value extraction module 330 is used to extract the gray value of pixels of a single-channel image
- the pixel defect result obtaining module 340 is used to input the pixel gray value of the single-channel image into the BP neural network model constructed based on the gradient descent algorithm to obtain the pixel defect detection result;
- the color difference defect result obtaining module 350 is configured to obtain the object color difference defect detection result of the object to be tested according to the pixel defect detection result.
- the above-mentioned object defect detection device further includes a model construction module for constructing an initial model of the BP neural network.
- the input layer of the initial model of the BP neural network is constructed by using the pixel gray value of the single-channel image as the input vector .
- the output layer of the BP neural network initial model is constructed from the pixel defect detection results of each object to be tested as the output vector, and the hidden layer of the BP neural network model is constructed from the mapping relationship between the input vector and the output vector; according to each object to be tested
- the gray value of the pixel of the single-channel image is trained on the initial model of the BP neural network through the gradient descent algorithm to obtain the BP neural network model.
- the above-mentioned model building module is also used to use the mean square error function mse to perform error analysis on the initial model of the BP neural network, and when the error value is less than the preset error, the BP neural network model is obtained.
- the multivariate image acquisition module 310 is also used to acquire images of the object to be measured;
- Convolution processing is performed on the image of the object to be measured to obtain a multi-scale image of the object to be measured; the multi-scale image of the object to be measured is converted into a multi-color image of the object to be measured.
- the single-channel image acquisition module 320 is also used to expand the multivariate color image to acquire the corresponding two-dimensional data matrix; to standardize the two-dimensional data matrix to acquire the covariance matrix; and to characterize the covariance matrix Value decomposition to obtain eigenvalues and eigenvectors; perform matrix conversion according to eigenvectors and eigenvalues to obtain feature images, which are multiple feature images; sequentially calculate the variance contribution rate of the feature image; select the features whose cumulative variance contribution rate reaches the preset threshold
- the image is a single-channel image.
- the above-mentioned object color difference defect detection device further includes a marking module, which is used to perform detection on the color difference defect area when the area of the color difference defect area formed by pixels with color difference defects in the pixel defect detection result is greater than a preset threshold. mark.
- the above-mentioned object color difference defect detection device further includes an early warning module for recording the number of color difference defect areas; according to the number of color difference defect areas, a defect warning message is sent.
- Each module in the above-mentioned object color difference defect detection device can be implemented in whole or in part by software, hardware and a combination thereof.
- the above-mentioned modules may be embedded in or independent of the processor in the computer equipment in the form of hardware, or stored in the memory in the computer equipment in the form of software, so that the processor can call and execute the operations corresponding to the above-mentioned modules.
- a computer device is provided.
- the computer device may be a server, and its internal structure diagram may be as shown in FIG. 4.
- the computer equipment includes a processor, a memory, a network interface and a database connected through a system bus. Among them, the processor of the computer device is used to provide calculation and control capabilities.
- the memory of the computer device includes a non-volatile storage medium and an internal memory.
- the non-volatile storage medium stores an operating system, a computer program, and a database.
- the internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium.
- the database of the computer equipment is used to store object color difference defect detection data.
- the network interface of the computer device is used to communicate with an external terminal through a network connection.
- the computer program is executed by the processor to realize an object color difference defect detection method.
- FIG. 4 is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied.
- the specific computer device may Including more or fewer parts than shown in the figure, or combining some parts, or having a different arrangement of parts.
- a computer device includes a memory and one or more processors.
- the memory stores computer-readable instructions.
- the one or more processors implement the methods provided in any of the embodiments of the present application. The steps of the method for detecting color defects of objects.
- One or more non-volatile computer-readable storage media storing computer-readable instructions.
- the one or more processors implement any one of the embodiments of the present application. Provides the steps of the detection method for object color difference defects.
- Non-volatile memory may include read only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory.
- Volatile memory may include random access memory (RAM) or external cache memory.
- RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous chain Channel (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.
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Abstract
Description
Claims (20)
- 一种物体色差缺陷检测方法,包括:获取待测物体的多元彩色图像;对所述多元彩色图像进行主成分分析,获取所述待测物体的单通道图像;提取所述单通道图像的像素点灰度值;将所述单通道图像的像素点灰度值输入基于梯度下降算法构建的BP神经网络模型,获取像素缺陷检测结果;及根据所述像素缺陷检测结果,得到所述待测物体的物体色差缺陷检测结果。
- 根据权利要求1所述物体色差缺陷检测方法,其特征在于,所述将所述单通道图像的像素点灰度值输入基于梯度下降算法构建的BP神经网络模型,获取像素缺陷检测结果之前,还包括:构建BP神经网络初始模型,所述BP神经网络初始模型的输入层由所述单通道图像的像素点灰度值作为输入向量构建,所述BP神经网络初始模型的输出层由所述各待测物体的像素缺陷检测结果作为输出向量构建,所述BP神经网络模型的隐含层由所述输入向量和输出向量之间的映射关系构建;及根据所述各待测物体单通道图像的像素点灰度值,通过梯度下降算法对所述BP神经网络初始模型进行训练,得到所述BP神经网络模型。
- 根据权利要求2所述物体色差缺陷检测方法,其特征在于,所述根据所述各待测物体单通道图像的像素点灰度值,通过梯度下降算法对所述BP神经网络初始模型进行训练,得到所述BP神经网络模型包括:采用均方误差函数mse对所述BP神经网络初始模型进行误差分析,当误差值小于预设误差时,得到所述BP神经网络模型。
- 根据权利要求1所述物体色差缺陷检测方法,其特征在于,所述获取待测物体的多元彩色图像包括:采集所述待测物体的图像;对所述待测物体的图像进行卷积处理,获取所述待测物体的多尺度图像;及将所述待测物体的多尺度图像转换为所述待测物体的多元彩色图像。
- 根据权利要求1所述物体色差缺陷检测方法,其特征在于,所述对多元彩色图像进行主成分分析,获取单通道图像包括:展开所述多元彩色图像,获取对应的二维数据矩阵;对所述二维数据矩阵进行标准化处理,获取协方差矩阵;对所述协方差矩阵进行特征值分解,获取特征值和特征向量;根据所述特征向量和所述特征值进行矩阵转换,获取特征图像,所述特征图像为多个;依次计算所述特征图像的方差贡献率;及选取累计方差贡献率达到预设阈值的所述特征图像作为所述单通道图像。
- 根据权利要求1所述物体色差缺陷检测方法,其特征在于,所述根据所述像素缺陷检测结果,得到所述待测物体的物体色差缺陷检测结果之后,还包括:当所述像素缺陷检测结果中具有色差缺陷的像素点构成的色差缺陷区域面积大于预设阈值时,对所述色差缺陷区域进行标记。
- 根据权利要求6所述物体色差缺陷检测方法,其特征在于,所述当所述像素缺陷检测结果中具有色差缺陷的像素点构成的色差缺陷区域面积大于预设阈值时,对所述色差缺陷区域进行标记之后,还包括:记录所述色差缺陷区域的数量;及根据所述色差缺陷区域的数量,发送缺陷预警消息。
- 一种物体色差缺陷检测装置,包括:多元图像获取模块,用于获取待测物体的多元彩色图像;单通道图像获取模块,用于对所述多元彩色图像进行主成分分析,获取所述待测物体的单通道图像;灰度值提取模块,用于提取所述单通道图像的像素点灰度值;像素缺陷结果获取模块,用于将所述单通道图像的像素点灰度值输入基于梯度下降算法构建的BP神经网络模型,获取像素缺陷检测结果;及色差缺陷结果获取模块,用于根据所述像素缺陷检测结果,得到所述待测物体的物体色差缺陷检测结果。
- 根据权利要求8所述的装置,其特征在于,所述多元图像获取模块还用于采集所述待测物体的图像;对所述待测物体的图像进行卷积处理,获取所述待测物体的多尺度图像;及将所述待测物体的多尺度图像转换为所述待测物体的多元彩色图像。
- 根据权利要求8所述的装置,其特征在于,所述单通道图像获取模块还用于展开所述多元彩色图像,获取对应的二维数据矩阵;对所述二维数据矩阵进行标准化处理,获取协方差矩阵;对所述协方差矩阵进行特征值分解,获取特征值和特征向量;根据所述特征向量和所述特征值进行矩阵转换,获取特征图像,所述特征图像为多个;依次计算所述特征图像的方差贡献率;及选取累计方差贡献率达到预设阈值的所述特征图像作为所述单通道图像。
- 一种计算机设备,包括存储器及一个或多个处理器,所述存储器中储存有计算机可读指令,所述计算机可读指令被所述一个或多个处理器执行时,使得所述一个或多个处理器执行以下步骤:获取待测物体的多元彩色图像;对所述多元彩色图像进行主成分分析,获取所述待测物体的单通道图像;提取所述单通道图像的像素点灰度值;将所述单通道图像的像素点灰度值输入基于梯度下降算法构建的BP神经网络模型,获取像素缺陷检测结果;及根据所述像素缺陷检测结果,得到所述待测物体的物体色差缺陷检测结果。
- 根据权利要求11所述的计算机设备,其特征在于,所述处理器执行所述计算机可读指令时还执行以下步骤:构建BP神经网络初始模型,所述BP神经网络初始模型的输入层由所述单通道图像的像素点灰度值作为输入向量构建,所述BP神经网络初始模型的输出层由所述各待测物体的像素缺陷检测结果作为输出向量构建,所述BP神经网络模型的隐含层由所述输入向量和输出向量之间的映射关系构建;及根据所述各待测物体单通道图像的像素点灰度值,通过梯度下降算法对所述BP神经网络初始模型进行训练,得到所述BP神经网络模型。
- 根据权利要求12所述的计算机设备,其特征在于,所述处理器执行所述计算机可读指令时还执行以下步骤:采用均方误差函数mse对所述BP神经网络初始模型进行误差分析,当误差值小于预设误差时,得到所述BP神经网络模型。
- 根据权利要求11所述的计算机设备,其特征在于,所述处理器执行所述计算机可读指令时还执行以下步骤:采集所述待测物体的图像;对所述待测物体的图像进行卷积处理,获取所述待测物体的多尺度图像;及将所述待测物体的多尺度图像转换为所述待测物体的多元彩色图像。
- 根据权利要求11所述的计算机设备,其特征在于,所述处理器执行所述计算机可读指令时还执行以下步骤:展开所述多元彩色图像,获取对应的二维数据矩阵;对所述二维数据矩阵进行标准化处理,获取协方差矩阵;对所述协方差矩阵进行特征值分解,获取特征值和特征向量;根据所述特征向量和所述特征值进行矩阵转换,获取特征图像,所述特征图像为多个;依次计算所述特征图像的方差贡献率;及选取累计方差贡献率达到预设阈值的所述特征图像作为所述单通道图像。
- 一个或多个存储有计算机可读指令的非易失性计算机可读存储介质,所述计算机可读指令被一个或多个处理器执行时,使得所述一个或多个处理器执行以下步骤:获取待测物体的多元彩色图像;对所述多元彩色图像进行主成分分析,获取所述待测物体的单通道图像;提取所述单通道图像的像素点灰度值;将所述单通道图像的像素点灰度值输入基于梯度下降算法构建的BP神经网络模型,获取像素缺陷检测结果;及根据所述像素缺陷检测结果,得到所述待测物体的物体色差缺陷检测结果。
- 根据权利要求16所述的存储介质,其特征在于,所述计算机可读指令被所述处 理器执行时还执行以下步骤:构建BP神经网络初始模型,所述BP神经网络初始模型的输入层由所述单通道图像的像素点灰度值作为输入向量构建,所述BP神经网络初始模型的输出层由所述各待测物体的像素缺陷检测结果作为输出向量构建,所述BP神经网络模型的隐含层由所述输入向量和输出向量之间的映射关系构建;及根据所述各待测物体单通道图像的像素点灰度值,通过梯度下降算法对所述BP神经网络初始模型进行训练,得到所述BP神经网络模型。
- 根据权利要求17所述的存储介质,其特征在于,所述计算机可读指令被所述处理器执行时还执行以下步骤:采用均方误差函数mse对所述BP神经网络初始模型进行误差分析,当误差值小于预设误差时,得到所述BP神经网络模型。
- 根据权利要求16所述的存储介质,其特征在于,所述计算机可读指令被所述处理器执行时还执行以下步骤:采集所述待测物体的图像;对所述待测物体的图像进行卷积处理,获取所述待测物体的多尺度图像;及将所述待测物体的多尺度图像转换为所述待测物体的多元彩色图像。
- 根据权利要求16所述的存储介质,其特征在于,所述计算机可读指令被所述处理器执行时还执行以下步骤:展开所述多元彩色图像,获取对应的二维数据矩阵;对所述二维数据矩阵进行标准化处理,获取协方差矩阵;对所述协方差矩阵进行特征值分解,获取特征值和特征向量;根据所述特征向量和所述特征值进行矩阵转换,获取特征图像,所述特征图像为多个;依次计算所述特征图像的方差贡献率;及选取累计方差贡献率达到预设阈值的所述特征图像作为所述单通道图像。
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