Component classification method and apparatus

Abstract

Embodiments of the invention disclose a component classification method and apparatus. The method comprises: inputting a to-be-classified component image into a trained convolutional neural network and calculating an advanced feature of the component image; calculating the probabilities of the component image belonging to the categories by utilizing the advanced feature; and taking the category corresponding to the maximum probability in the probabilities as the category of the component image. As the convolutional neural network can learn the advanced feature of the component image, when the component image is classified by utilizing the convolutional neural network, the collection of the component image is not restricted by scenes, the classification effect is good, and the accuracy is high; and meanwhile, as a local weight of the convolutional neural network is shared, in the process for classifying the component image by utilizing the convolutional neural network, the complexity in calculation can be lowered and the classification efficiency is high.

Description

technical field [0001] The invention relates to the field of computers, in particular to a component classification method and device. Background technique [0002] Printed circuit board (Printed circuit board, referred to as PCB board) refers to a circuit board that provides connections for various electronic components. As electronic equipment becomes more and more complex, the number of electronic components on the PCB board is also increasing. To detect the electronic components on the board, it is necessary to classify the electronic components, so as to automatically mark the electronic components, so as to reduce the workload of manual plate making, and also provide component information for subsequent component detection. [0003] At present, when classifying the component images captured from the PCB board, located at the component position, and containing a single component, it is mainly based on the traditional machine learning method to learn the characteristics ...

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Problems solved by technology

[0003] At present, when classifying the component images captured from the PCB board, located at the component position, and containing a single component, it is mainly based on the traditional machine learning method to learn the characteristics of the image, and then use this feature to classify the component image, but due to The characteristics of component images learned based on traditional machine learning methods are easily affected by the external environment, so in some scenarios, such as uneven illumination, the classification effect of component images will be poor

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