Abstract: A method of detecting product defects obtains an image of a product and sets a region of interest (ROI) of the image. A first contour of a first target object is detected in the region of interest. The image is detected according to the first contour to obtain a corrected image. A position difference between the first contour and a second target object in the region of interest is obtained. A second contour of the second target object is detected in the corrected image according to the position difference. A first image area corresponding to the first contour and a second image area corresponding to the second contour are segmented and input into an autoencoder. According to outputs of the autoencoder, whether the product is defective is determined. A detection result of the product is output. The method can detect defects on products quickly and accurately.

Abstract: A method for detecting cells in images using an autoencoder, a computer device, and a storage medium extracts a first feature vector from each of a plurality of sample medical images. The first feature vector is inputted into an autoencoder, and a first latent feature of each of the plurality of sample medical images is extracted. A first predicted value of a number of cells in each of the plurality of sample medical images is generated based on the first latent feature. The first latent feature is inputted into the autoencoder, and a plurality of reconstructed images are obtained. The autoencoder is optimized based on the plurality of reconstructed images and the first predicted value. This method can be run in the computer device to improve efficiency of detection from images.

Abstract: A benign tumor development trend assessment system includes an image outputting device and a server computing device. The image outputting device outputs first/second images captured from the same position in a benign tumor. The server computing device includes an image receiving module, an image pre-processing module, a target extracting module, a feature extracting module and a trend analyzing module. The image receiving module receives the first/second images. The image pre-processing module pre-processes the first/second images to obtain first/second local images. The target extracting module automatically detects and delineates tumor regions from the first/second local images to obtain first/second region of interest (ROI) images. The feature extracting module automatically identifies the first/second ROI images to obtain at least one first/second features. The trend analyzing module analyzes the first/second features to obtain a tumor development trend result.

Abstract: A method of grouping certain cell densities to establish the number and volume of cells appearing in an image input the image into a self-encoder having a preset number of a density grouping models to obtain a preset number of reconstructed images. The image and each reconstructed image are input into a twin network model of the density grouping model corresponding to each reconstructed image, and a first error value is calculated between the image and each reconstructed image. A minimum first error value in the first error value set is determined, and a density range corresponding to the density grouping model corresponding to minimum first error value is taken as the density range. An electronic device and a non-volatile storage medium performing the above-described method are also disclosed.

Abstract: A method for discovering defects in products by detecting abnormalities in images, an electronic device, and a storage medium are provided. The method includes training an autoencoder model using images of flawless products, inputting such an image into the autoencoder model, and determining whether a reconstructed image can be generated based on the image. The image is determined to be showing abnormality in respond that no reconstructed image is generated. In respond that the reconstructed image is generated, the reconstructed image corresponding to the image to be detected is obtained, and the presence of abnormality in the reconstructed image is determined according to a defect judgment criterion. This method running in the electronic device improves efficiency and accuracy of abnormality detection.

Abstract: A method for neural network model encryption and decryption includes a first apparatus stores a neural network model and obtains hardware configuration information of the first apparatus, and obtains an encryption key accordingly; encrypts the neural network model by a predetermined encryption algorithm; a second apparatus obtains the encrypted neural network model from the first apparatus, transmits a decryption request to the first apparatus, obtains the hardware configuration information from the first apparatus, obtains a decryption key based on the hardware configuration information; and decrypts the encrypted neural network model by a predetermined decryption algorithm.

Abstract: A method for generating defective image of products applied in an electronic device includes generating first input data according to flawless sample images and a first noise vector, using an autoencoder as a generator of a Generative Adversarial Network (GAN), inputting the first input data to the generator, and generating images for training in defects. The method further includes calculating a first loss value between the flawless sample images and the defect training images, inputting the defect training images into a discriminator of the GAN, and calculating a second loss value. The method further includes obtaining an optimized GAN and taking the optimized GAN as a defective image adversarial network, obtaining flawless testing images, inputting the flawless testing images and a second noise into a generator of the defective image adversarial network, and generating images of defects by processing the flawless testing images and the second noise.

Abstract: A method for augmenting defect sample data thereof includes acquiring a positive sample image and defect category information of a surface of a product; inputting the positive sample image and the defect category information to a generative adversarial network (GAN); and generating defect sample data corresponding to the defect category information. An apparatus and a non-transitory computer readable medium for augmenting defect sample data are also disclosed.

Abstract: A method for determining a height of a plant, an electronic device, and a storage medium are disclosed. In the method, a target image is obtained by mapping an obtained color image with an obtained depth image. The electronic device processes the color image by using a pre-trained mobilenet-ssd network, obtains a detection box appearance of the plant, and extracts target contours of the plant to be detected from the detection box. The electronic device determines a depth value of each of pixel points in the target contour according to the target image. Target depth values are obtained by performing a de-noising on depth values of the pixel points, and a height of the plant to be detected is determined according to the target depth value. The method improves accuracy of height determination of a plant.

Abstract: A method for determining a growth height of a plant, an electronic device, and storage medium are provided. The method includes controlling a camera device to capture a plant to be detected, and obtaining a color image and a depth image of the plant to be detected. The color image and the depth image are aligned and an alignment image is obtained. The color image is detected using a pre-trained mobilenet-ssd network, and a detection box including the plant to be detected is obtained. A depth value of each of pixel points in the detection box is determined, and target depth values are obtained. A mean value and a standard deviation of the target depth values are determined, and a height of the plant to be detected is determined. According to the method, accuracy of the height of the plant can be improved.

Abstract: A method for determining a plant growth curve includes obtaining color images and depth images of a plant to be detected at different time points, performing alignment processing on each color image and each depth image to obtain an alignment image, detecting the color image through a pre-trained target detection model to obtain a target bounding box, calculating an area ratio of the target bounding box in the color image, determining a depth value of all pixel points in the target boundary frame according to the aligned image, performing denoising processing on each depth value to obtain a target depth value, generating a first growth curve of the plant to be detected according to the target depth values and corresponding time points, and generating a second growth curve of the plant to be detected according to the area ratios and the corresponding time points.

Abstract: A method for measuring a growth height of a plant, an electronic device, and a storage medium are provided. The method controls a camera device to obtain a color image and a depth image of a plant to be detected. The color image is detected by a detection model which is pre-trained, and a plurality of detection boxes which includes a plurality of plants to be detected is obtained. The color image and the depth image are aligned to create an alignment image. A plurality of target boxes is acquired from the alignment image, and depth values of the plurality of target boxes are determined. The quantity of the target boxes and a height of one or more plants to be detected are determined, no manual operations are required.

Abstract: A method of determining a density of cells in a cell image, an electronic device and a storage medium are disclosed. The method acquires a cell image and extracts mapped features of the cell image by an autoencoder. The mapped features are inputted into a neural network classifier to obtain a feature category and a density range corresponding to the feature category is obtained. The density range is output. The present disclosure can improve n efficiency of obtaining a density of cells in a cell image.

Abstract: A method for detecting defects in products from images thereof and an electronic device applying the method inputs a defect image repair data set into an autoencoder to train the autoencoder, and generates a reconstructed image, calculates a reference error value between the sample image and the reconstructed image by a preset error function, and set a threshold value based on the reference error value. The electronic device inputs an image possibly revealing a defect into the autoencoder and generates the reconstructed image corresponding to the image to be detected, and uses the preset error function to calculate the reconstruction error between the image and the reconstructed image, thereby determining whether the image being analyzed does reveal defects. When the reconstruction error is greater than the threshold value, a determination is made that a defect is revealed.

Abstract: A method of determining a distribution of stein cells in a cell image, an electronic device and a storage medium are disclosed. The method acquires a cell image and segments the cell image and obtaining a plurality of sub-images. The plurality of sub-images is inputted into a stein cell detection model to detect to obtain a number of stein cells in each sub-image. A position of each sub-image in the cell image is determined. A distribution of the stein cells in the cell image is output, according to the number of stein cells in each sub-image and the position of each sub-image in the cell image. The present disclosure an accuracy of the distribution of stein cells in the cell image.

Abstract: In a product defect detection method, a detection image of a product is obtained. A first preset number of detection blocks are cut out from the detection image. The detection blocks are input into an auto-encoder to obtain reconstructed blocks and a mean square error between each detection block and the corresponding reconstructed block is calculated, an association between the mean square error and the detection block being established. Whether the product carries defective is determined according to the mean square error of each detection block. The method improves accuracy of detecting defects of the product.

Abstract: A method for classifying cells densities by cell images being input into artificial computer intelligence obtains positional information of all central points of all groups of first encoding features generated when training a model of convolutional neural network and ranges of densities of images of biological cells represented by different central points. The method inputs a test image of the biological cells into a trained model of the convolutional neural network to encode the test image, to obtain a second encoding feature. The method also determines a central point nearest to the second encoding feature according to the positional information. The method determines a range of densities of the test image according to the ranges of densities of the images represented by different central points and the central point nearest to the second encoding feature. An electronic device and a non-transitory storage medium are also disclosed.

Abstract: A method for calculating a density of stem cells in a cell image and an electronic device are provided. A plurality of preset ratios and a plurality of density calculation models can be used to perform hierarchical density calculations on the cell image. Starting from the largest preset ratio (the first preset ratio) reduction of the cell image to no reduction, the density calculation is performed on the cell image using a model starting with a highest density calculation (the first density calculation model) to a model with the smallest density calculation (the third density calculation model), which can quickly detect densities of various stem cells. Using different preset ratios and corresponding density calculation models for calculation, it is not necessary to calculate the number of stem cells to obtain the density of stem cells, which improves a calculation efficiency of the density of stem cells.

Abstract: A method for classifying cells densities by cell images being input into artificial computer intelligence inputs an image of biological cells as a test image into one or more trained models of convolutional neural network until a reconstructed image of the biological cells generated by one trained model matches with the test image. Each of the trained models of the convolutional neural network corresponds to one certain density range in which cell densities of images of the biological cells are found. The method also determines that a cell density of the test image is within the density range corresponding to the trained model of the convolutional neural network for which the reconstructed image of the biological cells and the test image match. A related electronic device and a non-transitory storage medium are also disclosed.

Abstract: A method for optimizing the conversion of a deep learning model to process other data, applied in a device, includes converting a first deep learning model to obtain a second deep learning model, obtaining a weighting arrangement of the two models according to their deep learning frameworks and performing a quantization on the two models. A similarity in weighting between the two models is analyzed to produce a weighting analysis based on the first and second weighting arrangement and the first and second model quantization result weighting. The two models are tested to establish a model performance analysis. One or more suggestions for optimization are obtained based on the weighting analysis and the model performance analysis, and are applied to optimize the second deep learning model, an optimized second deep learning model being employed to process the other data.