A board inspection apparatus is disclosed, which includes one surface-side irradiator that irradiates a first area on a surface side of a board with first light, a surface-side camera that takes an image of the first area, one rear face-side irradiator that irradiates a second area on a rear face side of the board with second light, a rear face-side camera that takes an image of the second area; and a controller that inspects the first area based on image data obtained from the surface-side camera and the second area based on image data obtained from the rear face-side camera.

In order to reduce inspection tact time in centralized management of a plurality of inspection lines, the system includes: a plurality of inspection lines 4 each having a visual appearance inspection device 3; a first database 5A for storing a captured image captured by each of the visual appearance inspection device 3; and a centralized control device 6 connected to each of the visual appearance inspection devices 3 and the first database 5A, having a display unit 32 that displays an inspection image of an inspection object 7, and enabling visual inspection of the inspection object 7 conveyed on each of the inspection lines 4 in a centralized manner; wherein, while one of the inspection lines 4 is inspected by using the centralized control device 6, the visual appearance inspection devices 3 of the other inspection lines 4 pre-captures the inspection object 7 conveyed on the inspection line 4 under predetermined capturing conditions and stores the pre-captured image in the first database 5A, and wherein the pre-captured image read from the first database 5A is displayed on the display unit 32 in the visual inspection.

The present disclosure proposes an inspection apparatus. The inspection apparatus may include: a structured-light source configured to sequentially radiate a plurality of structured lights having one phase range; a lens configured to adjust, for each of the plurality of structured lights, optical paths of light beams corresponding to phases of the phase range such that a light beam corresponding to one phase of the phase range arrives at each point of a partial region on an object; an image sensor configured to capture a plurality of reflected lights generated by the structured lights being reflected from the partial region; and a processor configured to acquire a light quantity value of the reflected lights; and derive an angle of the surface by deriving phase values of the reflected lights based on the light quantity value for the reflected lights.

A defective soldering point intensive extent analysis system for solder paste inspection and a method thereof are disclosed. After the circuit board is set with soldering pastes, the solder paste inspection can immediately detect a circuit board to generate a detection log, the information of defective soldering points in the detection log is analyzed to determine an aggregate of the defective soldering points set on the circuit board, so as to find a defective soldering point area, and generate and display defective soldering point alert information according to statistics of the defective soldering point area, thereby achieving the technical effect of conveniently analyzing the defective soldering points on the circuit board for accurate repair.

A circuit board detection method includes obtaining an input circuit board image, performing a detection on designated components of a circuit board in the circuit board image according to a preset detection method, determining whether a designated component in the circuit board image that fails the detection is allowed to shift within a preset angle range, and determining that the circuit board passes the detection when the designated component that fails the detection is allowed to shift within the preset angle range. The designated components include one or both of silkscreened components and non-silkscreened components.

Various defects in an electronic assembly can be intelligently identified with a system having at least a server connected to a first capture module and a second capture module. The first capture module may be positioned proximal a first manufacturing line while the second capture module is positioned proximal a second manufacturing line. Images can be collected of first and second electronic assemblies by respective first and second capture modules prior to the images being sent to a classification module of the server where at least one defect is automatically detected in each of the first and second electronic assemblies concurrently with the classification module.

An inspection system including an illumination subsystem and an image sensing subsystem, the illumination subsystem providing a plurality of illumination modalities, the system simultaneously illuminating at least two areas of an object with different ones of the plurality of illumination modalities, images of which are acquired by a single sensor forming part of the image sensing subsystem.

A substrate inspection apparatus generates, when anomalies of a plurality of second solder pastes among a plurality of first solder pastes printed on a first substrate is detected, at least one image indicating a plurality of second solder pastes with anomalies detected by using an image about a first substrate, applies the at least one image to a machine-learning-based model, acquires a plurality of first values indicating relevance of respective first fault types to the at least one image and a plurality of first images indicating regions associated with one of a plurality of first fault types, determines a plurality of second fault types, which are associated with the plurality of second solder pastes by using the plurality of first values and the plurality of first images, and determines at least one third solder paste, which is associated with the respective second fault types.

A method comprises obtaining a plurality of 2-dimensional gray scale images of a portion of a printed circuit board assembly. Each 2-dimensional gray scale image corresponds to one of a plurality of parallel planes intersecting the portion of the printed circuit board assembly at respective different locations. The method further comprises converting the plurality of 2-dimensional gray scale images into a color image. Each of the plurality of 2-dimensional gray scale images corresponds to and is used as input for a respective color channel of the color image. The method further comprises analyzing the color image to detect variation in color that indicates a defect; and outputting an alert indicating the defect in response to detecting the variation in color.

The present application discloses a method of training a convolutional neural network for defect inspection. The method includes collecting a training sample set including multiple solder joint images. A respective one of the multiple solder joint images includes at least one solder joint having one of different types of solder joint defects. The at least one solder joint is located substantially in a pre-defined region of interest (ROI) in a center of the image. The method further includes inputting the training sample set to a convolutional neural network to obtain target feature vectors respectively associated with the multiple solder joint images. Additionally, the method includes adjusting network parameters characterizing the convolutional neural network through a training loss function based on the target feature vectors and pre-labeled defect labels corresponding to different types of solder joint defects. The training loss function includes at least two different loss functions.