A method and device for testing product quality are disclosed. The method for testing product quality comprises: acquiring an image of a product to be tested; testing the image by using a pre-trained neural network model to obtain a testing result output by the neural network model; when the testing result indicates that the product to be tested is a defective product, performing a secondary judgment on the testing result according to position information of defective feature pixels in the image in the testing result, and determining whether the product to be tested is qualified according to a secondary judgment result. The method has high test accuracy, ensures the quality of product and facilitates reducing the labor cost of test.

A tire-enhancement product has a container comprising a dissolvable packaging material; and a solute encased in the container that is inert to the solute. The container is configured to be placed in an interior volume of a tire, to which solvent can be added. The container is configured to dissolve when placed in a predetermined solvent, and the solute is configured to mix with the solvent to form a tire-enhancement mixture.

A system and method for statistical analysis of PV photovoltaic panel images for quantifying the percentage of reduction of electricity generation or the percentage of electricity generation of PV photovoltaic panels due to the soiling level of PV photovoltaic panels by means of a system for statistical analysis of PV photovoltaic panel images, using a camera capable of photographing the panels in the visible spectrum, a clean and properly functioning PV photovoltaic panel without the presence of soiling, shading, or operational faults, a string of soiled PV photovoltaic panels to be evaluated, a computer and an method for image analysis.

The present disclosure provides an electronic apparatus capable of capturing an image without being affected by an abnormality on a display surface.

The electronic apparatus includes: a display unit; an imaging unit that is disposed on an opposite side to a display surface of the display unit; an abnormality detection unit that detects an abnormality on the display surface; and a display control unit that highlights a position where the abnormality detected by the abnormality detection unit occurs on the display unit.

This application relates to a battery test apparatus and an operating method thereof to check whether the direction of battery cells provided inside a battery tray is correct by implementing a computer vision AI algorithm. In one aspect, the method includes performing a first test for checking whether a battery tray has been correctly aligned with the battery test apparatus. The method may also include capturing a battery tray image including a plurality of battery cells using a camera upon alignment of the battery tray. The method may further include obtaining related information on battery cells from the battery tray image, and loading the related information on the battery cells. The method may further include performing a second test for checking whether directions of the battery cells are correct.

A system can receive downhole acquisition data relating to a wellbore. The system can pre-process the downhole acquisition data. The system can generate an incomplete borehole image using the downhole acquisition data. The system can determine a sparse representation based on the incomplete borehole image by performing an optimization with respect to the incomplete borehole image. The system can generate a complete borehole image based on an inverse of the sparse representation.

An apparatus for optical inspection of sanitaryware includes: a supporting device configured to receive a piece of sanitaryware to be inspected; at least one camera, configured to capture a plurality of images of the piece of sanitaryware under inspection; an automatic arm, movable between a plurality of operating positions relative to the supporting device, wherein the at least one camera is mounted on the automatic arm; a control unit, configured to receive the plurality of images, wherein the control unit is configured to process the plurality of images as a function of reference data to provide diagnostic information regarding the defectiveness of the piece of sanitaryware.

A method for automatically reconstituting the architecture, along a reinforcing axis, of the reinforcement of a composite material, includes acquiring images of the reinforcement of the composite material, each image being acquired along a section plane perpendicular to the reinforcing axis; for each image acquired, detecting, using a neural network, barycentre and/or the circumference of each section of the reinforcing thread; for at least one acquired reference image, assigning a tag corresponding to a reinforcing thread, to each detected barycentre or circumference; for each other acquired image, assigning, to each detected barycentre and/or each detected circumference, the tag of the corresponding barycentre in the acquired reference image; reconstituting the architecture of each reinforcing thread from each detected barycentre and/or circumference having the tag of the reinforcing thread and the position on the reinforcing axis associated with the acquired image on which the barycentre and/or the circumference has been detected.

A method of identifying a defect in a wet film comprises conveying said wet film (20), in a wet state, on a conveyor (10), providing a laser projection (1511) onto the wet film, acquiring a series of images, each depicting an area of the wet film, wherein at least a portion of the laser projection is visible, and using at least some of said images to identify said defect. There is also disclosed a method and device for producing a film.

The present disclosure provides a method for incrementing a sample image, an electronic device, and a computer readable storage medium. A specific implementation comprises: acquiring a first convolutional feature of an original sample image; determining, according to a region generation network and the first convolutional feature, a candidate region and a first probability that the candidate region contains a target object; determining a target candidate region from the candidate region based on the first probability, and mapping the target candidate region back to the original sample image to obtain an intermediate image; and performing image enhancement processing on a portion of the intermediate image corresponding to the target candidate region and/or performing image blur processing on a portion of the intermediate image corresponding to a non-target candidate region to obtain an incremental sample image.