Electronic component quality detection method and system based on deep learning

Abstract

The invention relates to a convolutional neural network electronic component quality detection method based on deep learning, and belongs to the technical field of fault diagnosis and signal processing and analysis. The method comprises the following steps: firstly, searching images of unqualified electronic components, such as component missing and wrong marking, dividing the collected images into a training set, a verification set and a test set, and carrying out unqualified region marking on the images in a data set, including coordinate information and classification information; secondly,constructing a convolutional neural network model for electronic component quality detection; training a convolutional neural network model for detecting the images of the unqualified components by utilizing the images in the training data set; performing quality detection on the unqualified component images in the test data set by using the trained convolutional neural network model for crack image detection. According to the method disclosed in the invention, the network model can effectively increase the selection of unqualified components, the speed is faster than that of a traditional multi-step image detection method, and more images can be processed in a short time; the network model can obtain finer local details; therefore, the whole network can realize effective progressive feature transmission, and the quality detection precision of the electronic components of the network model is improved.

Description

technical field [0001] The invention relates to a quality detection method of convolutional neural network electronic components based on deep learning, and belongs to the technical field of fault diagnosis technology and signal processing analysis. Background technique [0002] Common unqualified quality of electronic components are: fracture or missing column feet, wrong labeling information, and cracks on the surface of components due to special properties of materials and during processing. Any defects in the subsequent manufacturing and application of precision electronic components may cause them to fail to work properly, or even cause major accidents, resulting in catastrophic consequences. Therefore, timely detection of quality defects of electronic components plays a vital role in improving the product qualification rate and application scope of manufacturers. [0003] However, due to technical limitations, the realization of traditional detection methods, such as ...

AI Technical Summary

Problems solved by technology

However, although YOLO V3 is currently a relatively balanced network in terms of speed and accuracy, it is essentially an increase in speed at the cost of loss of accuracy. With the continuous development of hardware levels, this solution is not only difficult to win in terms of speed, but also Accuracy requirements cannot be guaranteed

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