Abstract: The present disclosure provides a detection method. The detection method includes inputting an image to be detected into a detection model being pre-constructed and detecting the image to be detected. The detection model includes a defect classification identification sub-model configured to identify a classification of a defect in the image to be detected, and the defect classification identification sub-model comprises a plurality of base models and a secondary model. The present disclosure further provides an electronic device and a non-transitory computer-readable storage medium.

Abstract: The present disclosure provides a detection device of a display panel. The detection device includes: an image receiver configured to receive a detection image of a display panel to be detected; a detector configured to input the detection image of the display panel to be detected into a detection model and generate a detection result by the detection model, the detection model is pre-constructed and configured to detect the display panel. The disclosure also provides a detection method of the display panel, an electronic device and a computer readable medium.

Abstract: A distributed computing system for product defect analysis is disclosed. The distributed computing system for product defect analysis includes a computing cluster for processing product manufacturing messages, a computing cluster for identifying product defect, a product image database, and a client device.

Abstract: A method and device for detecting a defect and method for training a model are provided. The method for detecting the defect includes: acquiring a sample data set and identifying feature information of the sample data set; acquiring an initial model; configuring a training parameter based on the feature information; obtaining a target model by training, according to the training parameter, the initial model with the sample data set; and obtaining defect information of a product by inputting real data of the product into the target model. The training parameter includes at least one of a learning rate descent strategy, a total number of training rounds and a test strategy, the learning rate descent strategy includes a number of learning rate descents and a round number when a learning rate descends, and the test strategy includes a number of tests and a round number when testing.

Abstract: A distributed computing system for product defect analysis is disclosed. The distributed computing system for product defect analysis includes a computing cluster for processing product manufacturing messages, a computing cluster for identifying product defect, a product image database, and a client device.

Abstract: A method and a device for processing product manufacturing messages, and an electronic device are disclosed. The method for processing product manufacturing messages includes: monitoring a plurality of product manufacturing messages; establishing a product defect analysis task queue based on the plurality of product manufacturing messages; distributing product defect analysis tasks to product manufacturing assisting devices based on the product defect analysis task queue, wherein the product defect analysis tasks include a task of identifying product defect content based on a defect identification model; wherein the product defect content includes any one or more of: product defect type, product defect location, and product defect size.

Abstract: The present disclosure provides a method and device for detecting an image category. The method includes: acquiring a sample data set including a plurality of sample images labeled with a category, the sample data set including a training data set and a verification data set; training a deep learning model using the training data set to obtain, according to different numbers of training rounds, at least two trained models; testing the at least two trained models using the verification data set to generate a verification test result; generating, based on the verification test result, a verification test index; determining, according to the verification test index, a target model from the at least two trained models; and predict a to-be-tested image of the target object using the target model to obtain the category of the to-be-tested image.

Abstract: The present disclosure relates to a task processing method and device based on defect detection, a computer readable storage medium, and a task processing apparatus . The method includes receiving a detection task; determining a task type of the detection task; storing the detection task in a task queue if the task type is a target task type; and executing the detection task in a preset order and generating a feedback signal when a processor is idle. The detection task of the target task type includes an inference task and a training task. Executing the training task includes modifying configuration information according to a preset rule based on product information in the detection task; acquiring training data and an initial model according to the product information; and using the training data to train the initial model according to the configuration information to obtain a target model and store it in memory.

Abstract: An image marking method, apparatus and system, which relates to the technical field of image processing. The present disclosure includes, when the working mode of the first client is a first mode, receiving a first marking task assigned by a second client, on the condition that the image marking approach is the first marking approach, according to a neural network model, determining a first marking result corresponding to the first original image; on the condition that the image marking approach is the second marking approach, according to an unsupervised algorithm model, determining a second marking result corresponding to the first original image; on the condition that the image marking approach is the third marking approach, receiving a third marking result inputted by a user into the first original image; and sending a target marking result to the second client.

Abstract: The present disclosure provides a detection device of a display panel. The detection device includes: an image receiver configured to receive a detection image of a display panel to be detected; a detector configured to input the detection image of the display panel to be detected into a detection model and generate a detection result by the detection model, the detection model is pre-constructed and configured to detect the display panel. The disclosure also provides a detection method of the display panel, an electronic device and a computer readable medium.

Abstract: The application provides an image detection method, an image detection apparatus, and a patterning control method, the image detection method including: identifying an input image to obtain image feature data of the input image; comparing the image feature data with preset image feature data in a preset image feature database to obtain deviation data of the input image; wherein the input image is a pattern image of a patterned structure. By intelligently detecting the pattern image of the patterned structure, the accuracy of the detection is improved, thereby reducing the labor input cost.

Abstract: An inference computing apparatus comprises at least one processor and a memory with program instructions stored therein, the program instructions can be executed by the at least one processor to cause the inference computing apparatus to perform the following operations: receiving a first inference model from a model training apparatus, the first inference model being obtained through a model training by the model training apparatus based on a first training sample library, the first training sample library comprising training samples from historical data generated in a manufacturing stage; performing an inference computing on data to be processed generated in the manufacturing stage based on the first inference model to obtain the inference result which is sent to a user-side device; and evaluating performance of the first inference model to determine whether the first inference model needs to be updated, and if yes, updating the first inference model.

Abstract: The application provides an image detection method, an image detection apparatus, and a patterning control method, the image detection method including: identifying an input image to obtain image feature data of the input image; comparing the image feature data with preset image feature data in a preset image feature database to obtain deviation data of the input image; wherein the input image is a pattern image of a patterned structure. By intelligently detecting the pattern image of the patterned structure, the accuracy of the detection is improved, thereby reducing the labor input cost.