Abstract: A product defect detection method which includes acquiring a detection image of a product to be detected is provided. The method further includes dividing the detection image into a first preset number of detection blocks. Once a detection result of each detection block is obtained by inputting each detection block into a preset defect recognition model, according to a position of each detection block in the detection image, a detection result of the product is determined according to the detection result of each detection block.

Abstract: A method for detecting defects in images, is employed in a computer device, and stored in a storage medium. The method trains an autoencoder model using unblemished images, inputting an image to be detected into the autoencoder model, and obtaining a reconstructed image. An image error is calculated between the image to be detected and the reconstructed image, and the image error is inputted into a student's t-distribution and a calculation result is obtained. In response that the calculation result falls within a preset defect determination criterion range, the image to be detected is determined to be an unblemished image. In response that the calculation result does not fall within the preset defect determination criterion range, the image to be detected is determined to be a defective image. The method improves the efficiency and accuracy of defect detection.

Abstract: A method for detecting image abnormities, applied in an electronic device, and stored in a storage medium are provided, obtains images for analysis. The images are divided into a plurality of first divided images and a plurality of second divided images by reference to image size. A first abnormity score is obtained by inputting the image into a first pre-trained abnormity detection model. A plurality of second abnormity scores are obtained by inputting the first divided images into a second pre-trained abnormity detection model. A plurality of third abnormity scores are obtained by inputting the second divided images into a third pre-trained abnormity detection model. An abnormal type of the image is determined according to a preset abnormity database in response to an abnormity detected in the image, the method improves accuracy of defect detection.

Abstract: A method of detecting defects revealed in images of finished products includes importing flawless images into an autoencoder for model training and obtaining reconstructed images from them. The reconstructed images are compared with the flawless images to obtain groups of test errors. An error threshold is selected from the groups of the test errors according to preset rules. In practice, obtaining an image to be tested, and a reconstructed image to be tested, and an error to be tested; determining detection of the image to be tested according to the error and the error threshold; and importing the image into a classifier for defect classification if the image is found to reveal a defect. An electronic device and a non-volatile storage medium for performing the above-described method, are also disclosed.

Abstract: A model input size determination method, an electronic device and a storage medium are provided, the method includes acquiring a plurality of test images and a defect result; and encoding each test image to obtain an encoding vector. The encoding vector is decoded to obtain a reconstructed image, then a reconstruction error and a plurality of sub-vectors are calculated; the plurality of sub-vectors is inputted into a Gaussian mixture model, then a plurality of sub-probabilities, an estimated probability and a test error are determined; a detection result in the test image according to the test error and the corresponding error threshold are obtained; an accuracy according to the detection result and the defect result are determined, and an input size is selected from the plurality of preset sizes according to the accuracy. An accuracy of defect detection in manufacturing can be improved.

Abstract: A method applied in an electronic device for detecting and classifying apparent defects in images of products inputs an image to a trained autoencoder to obtain a reconstructed image, determines whether the image reveals defects based on a defect criterion for filtering out small noise reconstruction errors. If so revealed, the electronic device calculates a plurality of structural similarity values between the image and a plurality of template images with marked defect categories, determines a target defect category corresponding to the highest structural similarity value, and classifies the defect revealed in the image into the target defect category.

Abstract: A method for detecting product for defects implemented in an electronic device includes classifying a plurality of product images to be detected into linear images or non-linear images; performing dimension reduction processing on the product images after image classification according to a plurality of dimension reduction algorithms to obtain a plurality of dimension reduction data; determining an optimal dimension reduction data of the plurality of dimension reduction data; obtaining score data of the product image by inputting the optimal dimension reduction data into a Gaussian mixture model; comparing the score data with a threshold; determining whether the score data is less than the threshold; and determining that there is at least one defect in the product image in response that the score data is determined to be less than the threshold.

Abstract: In a method for defecting surface defects, a trained weighting generated when defect-free training samples are used to train an autoencoder and pixel convolutional neural network is obtained. A test encoding feature is obtained by inputting the trained weighting into the autoencoder and pixel convolutional neural network and a weighted autoencoder of the weighted autoencoder and pixel convolutional neural network encoding a test sample. The test encoding feature is input into a weighted pixel convolution neural network of the weighted autoencoder and pixel convolutional neural network to output a result of test. The test result is either no defect in the test sample or at least one defect in the test sample. Inaccurate determinations as to defects are thereby avoided. An electronic device and a non-transitory storage medium are also disclosed.

Abstract: In a method for defecting surface defects, a weighting generated when defect-free training samples are used to train an autoencoder and pixel convolutional neural network is obtained. A test encoding feature is obtained by inputting the weighting into the autoencoder and pixel convolutional neural network and encoding a test sample with a weighted autoencoder of the weighted autoencoder and pixel convolutional neural network. The test encoding feature is divided into many sub-test encoding features. The sub-test encoding features are input into a weighted pixel convolution neural network of the weighted autoencoder and pixel convolutional neural network one by one to output a result of test, the test result being either no defect in the test sample or at least one defect in the test sample. Inaccurate defect determinations are avoided, and accurate determinations even of fine defects improved. An electronic device and a non-transitory storage medium are also disclosed.

Abstract: A method for detecting data defects and a computing device applying the method obtains a test image for analysis. A field to which the test image relates is determined. Based on the field, a target convolutional layer is determined from a convolutional neural network. The target convolutional layer is used to extract features of the test image. A target score of the test image and a score threshold corresponding to the field are determined. If the target score is less than the score threshold, it is determined that the test image reveals defects, thereby improving an accuracy of defect detection.

Abstract: A method of detecting defects revealed in images of products obtains sample image training data. An underlying feature dimension of an autoencoder is selected and a score is obtained. By comparing the score with a standard score, an optimal underlying feature dimension is confirmed. A test image is inputted into the autoencoder with the optimal underlying feature dimension to obtain a reconstruction image. A reconstruction error between the test image and the reconstruction image is computed. By comparing the reconstruction error with the predefined threshold a result of analysis of the test image is outputted. An image defect detection apparatus and a computer readable storage medium applying the method are also provided.

Abstract: A method for detecting defects in multi-scale images and a computing device applying the method acquires a to-be-detected image and converts the to-be-detected image into a plurality of target images of preset sizes. Feature extraction is performed on each target image by using a pre-trained encoder to obtain a latent vector, the latent vector of each target image is inputted into a decoder corresponding to the encoder to obtain a reconstructed image and then into a pre-trained Gaussian mixture model to obtain an estimated probability. Reconstruction error is calculated according to each target image and the corresponding reconstructed image. A total error is calculated according to the reconstruction error of each target image and the corresponding estimated probability, and a detection result is determined according to the total error of each target image and a corresponding preset threshold, thereby improving an accuracy of defect detection.

Abstract: A machine learning model training method includes receiving a problem type to be processed, determining a machine learning model corresponding to the problem type, receiving sample data to train the determined machine learning model, analyzing training results of the determined machine learning model obtained after training is completed and displaying the training results that meet preset conditions, and providing a machine learning model corresponding to the training result that meets the preset conditions.

Abstract: An image defect detection method is used in an electronic device. The electronic device determines a training image feature set, and trains a Gaussian mixture model by using the feature set to obtain an image defect detection model and a reference error value. An image for analysis is input into the autoencoder to obtain a second implicit vector and a second reconstructed image, and to calculate a second reconstruction error. The electronic device obtains a test image feature of the image for analysis according to the second reconstruction error and the second implicit vector, and inputs the test image feature into the image defect detection model to obtain a prediction score. The image for analysis is determined to reveal a defect when the prediction score is less than or equal to the reference error value.

Abstract: A defect detection method based on an image of products and an electronic device can accurately determine the error threshold by determining the reconstruction error generated during image reconstruction and by determining the estimated probability generated by the Gaussian mixture model. The test error can then be compared with the error, since the test error and the error threshold are compared numerically, the existence of subtle defects are revealed in the product image, thereby improving the accuracy of defect detection.

Abstract: An image defect detection method used in an electronic device, calculates a Kullback-Leible divergence between a first probability distribution and a second probability distribution, and thereby obtains a total loss. Images of samples for testing are input into an autoencoder to calculate a second latent features of the testing sample images and the second reconstructed images. Second reconstruction errors are calculated by a preset error function, as is a third probability distribution of the second latent features, and a total error is calculated according to the third probability distribution and the second reconstruction errors. When the total error is greater than or equal to the threshold, determining that the images of samples for testing reveal defects, and when the total error is less than the threshold, determining that the images of samples for testing reveal no defects.

Abstract: A method for optimizing a data model is used in a device. The device acquires data information and selecting at least two data models according to the data information, and utilizes the data information to train the at least two data models. The device acquires each accuracy of the at least two data models, determines a target data model which has greatest accuracy between the at least two data models, and optimizes the target data model.

Abstract: A method for detecting defects in images, is employed in a computer device, and stored in a storage medium. The method trains an autoencoder model using unblemished images, inputting an image to be detected into the autoencoder model, and obtaining a reconstructed image. An image error is calculated between the image to be detected and the reconstructed image, and the image error is inputted into a student's t-distribution and a calculation result is obtained. In response that the calculation result falls within a preset defect determination criterion range, the image to be detected is determined to be an unblemished image. In response that the calculation result does not fall within the preset defect determination criterion range, the image to be detected is determined to be a defective image. The method improves the efficiency and accuracy of defect detection.

Abstract: A method for selecting a deep learning network which is optimal for solving an image processing task obtaining a type of the image processing task, selecting a data set according to the type of problem, and dividing selected data set into training data and test data. Similarities between different training data are calculated, and a batch size of the training data is adjusted according to the similarities of the training data. A plurality of deep learning networks is selected according to the type of problem, and the plurality of deep learning networks is trained through the training data to obtain network models. Each of the network models is tested through the test data, and the optimal deep learning network with the best test result is selected from the plurality of deep learning networks appropriate for image processing.

Abstract: A method for detecting product for defects implemented in an electronic device includes classifying a plurality of product images to be detected into linear images or non-linear images; performing dimension reduction processing on the product images after image classification according to a plurality of dimension reduction algorithms to obtain a plurality of dimension reduction data; determining an optimal dimension reduction data of the plurality of dimension reduction data; obtaining score data of the product image by inputting the optimal dimension reduction data into a Gaussian mixture model; comparing the score data with a threshold; determining whether the score data is less than the threshold; and determining that there is at least one defect in the product image in response that the score data is determined to be less than the threshold.