Abstract: The present application discloses a method for image text recognition, an apparatus, a device, and a storage medium, and relates to image processing technologies in the field of cloud computing. A specific implementation is: acquiring an image to be processed, where at least one text line exists in the image to be processed; processing each text line in the image to be processed to obtain a composite encoded vector corresponding to each word in each text line, where the composite encoded vector carries semantic information and position information; and determining a text recognition result of the image to be processed according to the semantic information and the position information carried in the composite encoded vector corresponding to each word in each text line. This technical solution can accurately distinguish adjacent fields with small pixel spacing in the image and improve the accuracy of text recognition in the image.

Abstract: Provided are a method and an apparatus for component fault detection based on an image, and a specific implementation is: when it is determined that an image shot by an image pickup apparatus for a component to be tested with a first shooting parameter does not meet a preset condition, adjusting the first shooting parameter to a second shooting parameter; controlling the image pickup apparatus to shoot for the component to be tested with the second shooting parameter to obtain a first image that meets the preset condition; and performing fault detection on the component to be tested according to the first image. The image pickup apparatus can be adjusted in real time, so that the image can be used for fault detection only when meeting the preset condition, thereby the image is kept stable, and the accuracy rate of component fault identification based on an image is improved.

Abstract: Embodiments of the present disclosure provide a context acquiring method based on voice interaction and a device, the method comprising: acquiring a scene image collected by an image collection device at a voice start point of a current conversation, and extracting a face feature of each user in the scene image; if it is determined that there is a second face feature matching a first face feature according to the face feature of each user and a face database, acquiring a first user identifier corresponding to the second face feature from the face database; if it is determined that a stored conversation corresponding to the first user identifier is stored in a voice database, determine a context of a voice interaction according to the current conversation and the stored conversation, and after the voice end point of the current conversation is obtained, storing the current conversation into the voice database.

Abstract: The method and apparatus for detecting a peripheral circuit of a display screen provided by the present disclosure receive a quality detection request sent by a console deployed on a production line of the peripheral circuit of the display screen, where the quality detection request includes a peripheral circuit image of the display screen captured by an image capturing device on the production line of the peripheral circuit of the display screen; zoom in or out on the peripheral circuit image of the display screen to obtain an image to be detected a size of which is consistent with an input size requirement of a defect detection model; input the image to be detected into the defect detection model to obtain a defect detection result; and determine quality of the peripheral circuit of the display screen according to the defect detection result.

Abstract: Embodiments of the present disclosure provide a method and apparatus for predicting a severe convection weather. The method may include: acquiring a current radar echo map sequence, the current radar echo map sequence being a radar echo map sequence within a current time period; generating, based on the current radar echo map sequence, a future radar echo map sequence, the future radar echo map sequence being a radar echo map sequence within a future time period; and inputting the future radar echo map sequence into a pre-trained severe convection weather predicting model to obtain a severe convection weather intensity predicting map, where the severe convection weather predicting model is used to predict the intensity of a severe convection weather.

Abstract: The disclosure provides a method for detecting a display screen peripheral circuit, by receiving a quality detection request sent by a console deployed on a production line of the display screen peripheral circuit, where the quality detection request includes an image of the display screen peripheral circuit captured by an image capturing device on the production line of the display screen peripheral circuit; enlarging or reducing the image of the display screen peripheral circuit to obtain an image to be detected, where the defect detection model is obtained by using a historical image of a defective display screen peripheral circuit to perform object detection FAST RCNN algorithm training; inputting the image to be detected into the defect detection model to obtain a defect detection result; and determining, according to the defect detection result, quality of a display screen peripheral circuit corresponding to the image of the display screen peripheral circuit.

Abstract: Provided are a method for detecting display screen quality, an apparatus, an electronic device and a storage medium. The method includes: receiving a quality detection request sent by a console deployed on a display screen production line, the quality detection request including a display screen image collected by an image collecting device on the display screen production line; inputting the display screen image into a defect detection model to obtain a defect detection result, the defect detection model being obtained by training historical defective display screen images using a structure of deep convolutional neural networks and an object detection algorithm; and determining, according to the defect detection result, a defect on a display screen corresponding to the display screen image, a defect category corresponding to the defect, and a position corresponding to the defect.

Abstract: The present disclosure provides a display screen quality detection method, an apparatus, an electronic device and a storage medium, where the method comprises: receiving a quality detection request sent by a console deployed on a display screen production line, where the quality detection request includes a display screen image captured by an image capturing device on the display screen production line, performing image preprocessing on the display screen image, and inputting the preprocessed display screen image into a defect detection model to obtain a defect detection result, where the defect detection model is obtained by training with a historical defect display screen image using a deep convolutional neural network structure and an instance segmentation algorithm, determining, according to the defect detection result, quality of a display screen corresponding to the display screen image.

Abstract: Embodiments of the present disclosure relate to a method and apparatus for outputting information. The method can include: acquiring an image of a to-be-inspected object; segmenting the image into at least one subimage; for a subimage in the at least one subimage, inputting the subimage into a pre-trained defect classification model to obtain a defect category corresponding to the subimage; and outputting defect information of the object based on a defect category corresponding to each subimage.

Abstract: The application provides a method and a device for detecting a defect in a steel plate, as well as an apparatus and a server therefor. The method for detecting a defect in a steel plate comprises: receiving image data of the steel plate, and generating a defect detection request according to the image data; monitoring computing loads of a plurality of servers, and sending the image data and the defect detection request to a first server; receiving, from the first server, a detection result obtained by calculating the image data using the detection model; and operating according to the detection result, wherein the detection result comprises a selected one of a pass result and a defect result. With the proposed method, the position and the classification of the at least one defect can be obtained, so that the detection accuracy is improved.

Abstract: Embodiments of the present disclosure provide a method and a device for detecting defect of a meal box, a server, a device, and a storage medium. The method includes: receiving a detection request including an image of the meal box sent by a user terminal, the image of the meal box being obtained by an image acquirer of the user terminal; and performing defect recognition based on the image of the meal box and a defect detection model in response to the detection request.

Abstract: Embodiments of the present disclosure disclose a method and apparatus for predicting a passenger flow. A specific implementation of the method includes: acquiring, based on a received prediction request for a passenger flow value of a target traffic route in a preset time period, route information of the target traffic route and a passenger flow sequence corresponding to a preset number of consecutive time periods before a current time; encoding the route information to generate encoded route information; and inputting the encoded route information and the passenger flow sequence into a pre-trained passenger flow prediction model to obtain a passenger flow prediction value of the target traffic route in the preset time period, the passenger flow prediction model being used to represent a corresponding relationship between both the encoded route information and the passenger flow sequence and the passenger flow prediction value of the target traffic route.

Abstract: Embodiments of the present disclosure provides a method and apparatus for inspecting burrs of an electrode slice. The method may include: acquiring a to-be-inspected electrode slice image; and inputting the to-be-inspected electrode slice image into a pre-trained burr instance segmentation model to obtain inspection result for characterizing whether the electrode slice displayed in the to-be-inspected electrode slice image has burrs and contour of the burrs, where the burr instance segmentation model is used to characterize the corresponding relationship between the electrode slice image and the inspection result and contour information. The method may further include: and outputting, in response to the inspection result for characterizing that the electrode slice displayed in the to-be-inspected electrode slice image has burrs, prompt information for characterizing that the electrode slice displayed in the to-be-inspected electrode slice image has burrs.

Abstract: Embodiments of the present disclosure provide a human-machine interaction processing method, an apparatus thereof, a user terminal, a processing server and a system. On the user terminal side, the method includes: receiving an interaction request voice inputted from a user, and collecting video data of the user when inputting the interaction request voice; obtaining an interaction response voice corresponding to the interaction request voice, where the interaction response voice is obtained according to expression information of the user when inputting the interaction request voice and included in the video data; and outputting the interaction response voice to the user. The method imbues the interaction response voice with an emotional tone that matches the current emotion of the user, so that the human-machine interaction process is no longer monotonous, greatly enhancing the user experience.

Abstract: The present disclosure provides a method, an apparatus, a device and a system for sign language translation, where a server receives video information sent by a terminal device, and preprocesses the video information to obtain at least one sign language action; the at least one sign language action is input into a sign language model for classification and prediction to obtain a word corresponding to the at least one sign language action; each word is input into a language model to determine whether an intention expression is complete; and each word is sent to the terminal device when the intention expression is complete, so that the terminal device displays each word, thereby realizing the translation of the sign language action into text, enabling the ordinary persons to better understand intentions of the hearing impaired, thus improving efficiency of communications.

Abstract: A method and an apparatus for inspecting a corrosion defect of a ladle are provided. The method includes: acquiring images from various angles using an image acquisition apparatus inside a to-be-inspected ladle; and inputting the acquired images into a defect inspection system to obtain a label representing a defect category, the defect inspection system including: a deep convolutional neural network that predicts a category of a corrosion defect of the ladle included in the images based on the input images. This method has the advantages of high safety, high accuracy and high real-time performance in inspecting the ladle status.

Abstract: Embodiments of the present disclosure disclose a method and apparatus for recommending a video. A specific implementation of the method includes: finding a recommended video corresponding to a target video from all candidate videos based on similarities of content characteristics of the videos, the target video being a video to be played on a terminal of a user; and sending play information of the recommended video corresponding to the target video to the terminal of the user. The method finds a video similar on video content to the target video that the user desires to view based on the content characteristic of the video, and recommends the video similar on video content to the target video that the user desires to view to the user.

Abstract: The present disclosure discloses a method and apparatus for scheduling a resource for a deep learning framework. The method can comprise: querying statuses of all deep learning job objects from a Kubernetes platform at a predetermined interval; and submitting, in response to finding from the queried deep learning job objects a deep learning job object having a status conforming to a resource request submission status, a resource request to the Kubernetes platform to schedule a physical machine where the Kubernetes platform is located to initiate a deep learning training task. The method can completely automate the allocation and release on the resource of the deep learning training task.

Abstract: The application provides a method and a device for detecting a defect in a steel plate, as well as an apparatus and a server therefor. The method for detecting a defect in a steel plate comprises: receiving image data of the steel plate, and generating a defect detection request according to the image data; monitoring computing loads of a plurality of servers, and sending the image data and the defect detection request to a first server; receiving, from the first server, a detection result obtained by calculating the image data using the detection model; and operating according to the detection result, wherein the detection result comprises a selected one of a pass result and a defect result. With the proposed method, the position and the classification of the at least one defect can be obtained, so that the detection accuracy is improved.