Circuit board defect detection method and device

Abstract

The present application provides a training method and device for a circuit board defect detection model, a detection method and a device, wherein the training method includes: obtaining at least one training sample pair used in the current round of training; wherein the training sample pair includes two identical and / or different circuit board images; each training sample pair in the obtained at least one training sample pair is input into the corresponding entrances of the branches of the neural network with two branches respectively, and the neural network is used for the input training sample pair respectively Determining the first feature and the second feature; comparing the comparison result obtained by comparing the first feature with the second feature, and matching the labeled value of the corresponding training sample pair; and performing the current round of training on the neural network according to the matching result; Multiple rounds of training on the neural network resulted in a circuit board defect detection model. The circuit board defect detection model trained by the present application can automatically detect whether there is a defect in the circuit board, and the fault tolerance rate and accuracy rate of the detection are high.

Description

technical field [0001] The present application relates to the technical field of circuit board detection, in particular, to a training method and device for a circuit board defect detection model, and a detection method and device. Background technique [0002] As a carrier for the electrical connection of electronic components, a printed circuit board (PCB) realizes specific circuit functions through multiple components mounted on it and the preset logical circuit connection relationship between these components. Considering that during the production, processing or assembly process of PCB boards, components may be missing or over-installed due to various situations, which will cause the PCB boards to fail to work or run normally. As the PCB board is the carrier of electronic products, these PCB boards that cannot work or run normally can only be detected to ensure the performance of electronic products. [0003] The earliest method of PCB board defect detection relies on ...

AI Technical Summary

Problems solved by technology

[0004] However, the above-mentioned method of detecting PCB board defects based on automatic optical detection means needs to set different matching templates for different error types, and the error types without matching templates cannot be effectively detected, resulting in a low error tolerance rate of detection. And in order to prevent missed detection, the above method often adjusts the threshold range of detection, which will make more correct PCB boards be considered wrong, and the accuracy rate is low

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