Abstract: The present disclosure provides a method and apparatus for processing a streaming media service, an electronic device, and a storage medium, and relates to the technical field of computers, particularly to technical fields such as industrial vision, deep learning, streaming media, and information flow. A specific implementation solution involves: acquiring registration information of an input source, the registration information including process information of a streaming media service process of the input source and streaming media address information of the input source; enabling the streaming media service process according to the process information; and controlling, by using the streaming media address information, the streaming media service process to process streaming media data of the input source.

Abstract: The present disclosure provides a method and apparatus for processing a streaming media service, an electronic device, and a storage medium, and relates to the technical field of computers, particularly to technical fields such as industrial vision, deep learning, streaming media, and information flow. A specific implementation solution involves: acquiring registration information of an input source, the registration information including process information of a streaming media service process of the input source and streaming media address information of the input source; enabling the streaming media service process according to the process information; and controlling, by using the streaming media address information, the streaming media service process to process streaming media data of the input source.

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: 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: 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: A display screen quality detection method, 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, 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. The technical solution has high defect detection accuracy, good system performance, and high business expansion capability.

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: 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: The present disclosure provides a battery detection method and a battery detection device. The method includes: obtaining a picture of each battery on a battery production line, and obtaining a corresponding production node; inputting the picture into a preset defect detection model, and obtaining a detection result output by the defect detection model, and when the detection result denotes that there is the defect on the picture, sending a control instruction to a control device of the production node corresponding to the picture, to cause the control device to shunt the battery corresponding to the picture having the defect based on the control instruction. The detection result includes whether there is a defect, a defect type, and a defect position.

Abstract: The present disclosure provides a battery detection method and a battery detection device. The method includes: obtaining a picture of each battery on a battery production line, and obtaining a corresponding production node; inputting the picture into a preset defect detection model, and obtaining a detection result output by the defect detection model, and when the detection result denotes that there is the defect on the picture, sending a control instruction to a control device of the production node corresponding to the picture, to cause the control device to shunt the battery corresponding to the picture having the defect based on the control instruction. The detection result includes whether there is a defect, a defect type, and a defect position.

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: 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: 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 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: 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 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: 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.