The application discloses a PCBA
foreign matter detection method based on a multi-resolution subspace projection, acquires a to-be-detected board and a standard reference board image, determines an adaptive down-sampling multiple based on a minimum
foreign matter physical area, performs
image registration with the aid of a
mask to eliminate geometric deformation, extracts a multi-resolution feature map through a pre-trained deep
convolutional neural network, calculates feature differences, and uses a dynamic threshold to screen a preliminary candidate
point set, constructs a local principal component subspace of a local neighborhood of a
reference image, performs first-stage screening by calculating the orthogonal projection error of a test
feature vector in the space, maps remaining candidate points to a multi-scale feature space and splices them, constructs a global multi-scale principal component subspace, performs second-stage screening through a global projection error, and outputs a final result selection, and through the implementation of the application, the problems of a high-definition
image computing power
bottleneck and a high
false alarm in comparison are solved, the application has zero-sample generalization capability, and sample-free training can accurately detect tiny foreign matters.