Hon Hai Precision Industry Co., Ltd.

Guo-Chin Sun

Chin-Pin Kuo

Tzu-Chen Lin

Tung-Tso Tsai

Wan-Jhen Lee

Yueh Chang

Chung-yu Wu

Chih-Te Lu

I-Hua Chen

Jung-Hao Yang

Wei-chun Wang

An image processing method and an electronic device are disclosed, the method acquires training data in response to receiving an image processing instruction and trains a deep learning model with the training data by using a preset deep learning framework to obtain an initial model. A data type of the initial model is converted to increase the data processing speed of the model. A correction layer is added to the converted initial model and the initial model is optimized by training weights of the correction layer to obtain an image processing model to further increase the data processing speed. After acquiring an image from the image processing instruction, the image processing model can be used to process the image and an image processing result can be outputted. The image is processed based on the optimized image processing model, and the data processing speed and accuracy are guaranteed.

Image processing method, electronic device and storage medium

In an image correction method, a distorted image and a standard image corresponding to the distorted image are obtained. The distorted image is divided into first image regions, the standard image into second image regions. For each first image region and corresponding second image region, first and second points respectively are selected. A relationship between the first image region and the second image region is determined according to differences between the first points and the second points. The first image region is corrected according to the relationship between the first image region and the second image region and corrected first image regions are stitched to obtain a corrected image of the distorted image. The image correction method improves the accuracy of image correction.

Image correction method and computing device utilizing method

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.

Method and device for optimizing deep learning model conversion, and storage medium

A method for loading multiple neural network model includes compiling at least two neural network models and generating at least two binary model files corresponding to the at least two neural network models. One of the at least two binary model files is an original model file which is taken as the basic model, and differences between the two files are calculated and recorded using preset difference calculation method. A differences file is generated, and the basic model and the differences file are compressed using a preset compression method, to generate an input file. Such input file is input into a neural network accelerator, the input file being decompressed to obtain the basic model and the differences file. The basic model and the differences file are loaded into the neural network accelerator. An electronic device and a non-volatile storage medium performing the above-described method are also disclosed.

Method for loading multiple neural network models and electronic device

For the benefit of pedestrians, a method for identifying and locating positions of obstacles moving on a pedestrian sidewalk acquires an image of the sidewalk and processes the image to divide it. The divided image comprises classifications of objects in the image on a pixel by pixel basis. The classifying of objects in the divided image comprises the sidewalk classification, and classification of the obstacles appears in the image. Pixels surrounding the obstacles are acquired in terms of number and classifications. Positions of the obstacles are determined based on a preset threshold, the classifications of adjacent pixels of the obstacles, and the pixel number of the adjacent pixel in each object classification. An apparatus and a system applying the method are also disclosed.

Method for locating position of obstacles, and apparatus, and system applying method

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.

Defect detection method, computer device and storage medium

A method for detecting a product for defects implemented in an electronic device includes detecting images of a product for defects by a first defect detection model in a preset period, and obtaining a detection result; when a ratio of the number of negative sample images is greater than a preset threshold, training an autoencoder model; obtaining historical positive sample images of the product, inputting the history positive sample images into the trained autoencoder model, and calculating a latent feature; inputting the latent feature of each history positive sample image into a decoding layer of the trained autoencoder model, and calculating newly added positive sample images; training the first defect detection model and obtain a second defect detection model; and inputting images of a product to be detected to the second defect detection model, and obtaining a detection result of the product.

Method for detecting product for defects, electronic device, and storage medium

In an image processing method, a detection image and a marked image are obtained. An image segmentation model is applied to segment a first segmented image from the detection image. The first segmented image is corrected according to the marked image to obtain a second segmented image. A size of the second segmented image is adjusted to obtain an adjusted segmented image. The adjusted segmented image is used as a standard segmented image of the detection image. The method improves accuracy of image segmentation and recognition.

Image processing method and computing device

A method of optimizing the detection of abnormalities in images of products generates a first image similar to training images and a second image similar to testing images with normal images and the images showing abnormalities inputted into a generative adversarial network (GAN). The GAN determines a first similarity ratio between the first image and the training image and generates a parameter based on the first similarity ratio for adjusting the GAN. A second similarity ratio between the second image and the testing image is determined. The testing image is deemed a normal image when the second similarity ratio is larger than the specified threshold value, and deemed to be an image revealing abnormalities when the second similarity ratio is less than or equal to the specified threshold value. A terminal device and a computer readable storage medium applying the method are also provided.

Method for optimizing detection of abnormalities in images, terminal device, and computer readable storage medium applying the method

A method, apparatus, and device for labeling images of PCBs includes obtaining an image to be tested; comparing the image to be tested to a reference image to generate an image mask, the image mask includes several connected domains; detecting defects of the image to be tested; when at least one defect is detected in the image to be tested, obtaining a coordinate of the at least one defect; based on a central coordinate of the connected domains and the coordinate of the at least one defect, determining the connected domains to be defect connected domains or normal connected domains; generating a first image mask and a second image mask; and processing the first image mask and the second image mask with the image to be tested to obtain a defect element image corresponding to the defect connected domains and a normal element image corresponding to the normal connected domains.

Method, apparatus, and device for labeling images

An image augmentation method applied to an electronic device is provided. The method includes constructing a variational learner and a discriminator based on a fully convolutional neural network. A target image is obtained by inputting a gas leakage image into the variational learner. A variational autoencoder model is obtained by training the variational learner based on a discrimination result of the discriminator on the target image. A reconstruction accuracy rate is calculated based on a test image, an augmented model is obtained by adjusting the variational autoencoder model based on the gas leakage image, in response that the reconstruction accuracy rate being less than a preset threshold; and an augmented image is obtained by inputting the image to be augmented into the augmented model.

Image augmentation method, electronic device and readable storage medium

In a method of distinguishing objects in images, a first image segmentation model is applied to segment a first segmented image including a first object from a test image. A second image segmentation model is applied to segment a second segmented image including a second object from the test image. A third segmented image marking the first object and the second object is obtained according to first coordinates of the first object in the first segmented image and/or second coordinates of the second object in the second segmented image. The method can segment different objects from an image quickly and accurately.

Method of distinguishing objects in images, computing device, and storage medium

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.

Machine learning model training method and machine learning model training device

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.

Method for optimizing a data model and device using the same

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.

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Taipei, Taiwan

Guo-Chin Sun