Micro part quality detection system based on convolutional neural network

Abstract

The invention discloses a micro part quality detection system based on a convolutional neural network. The micro part quality detection system comprises: A, collecting the surface image information ofa micro part by an image acquisition module formed by microscopic vision; B, detecting the image collected by the microscopic vision by using a convolutional neural network model, and classifying thedetected defect images; C, transmitting the classifying result into a main controller, and sending a control signal to a terminal actuator; and D, carrying out picking and classifying on the corresponding micro part by the terminal mechanical arm actuator according to the control signal transmitted by the controller so as to convey the part into the corresponding receiving box, such that the whole system completes the detection and defect classification on the surface quality of the micro part. According to the present invention, the system can effectively used for the detection of micro parts, and can improve the automation degree and the efficiency of detection, and reduce the influence of human factors on the detection process and the labor intensity of workers.

Description

technical field [0001] The invention relates to the technical field of micro-part detection, in particular to a micro-part quality detection system based on a convolutional neural network. Background technique [0002] With the continuous development of China's industry, research in the field of micro-nano has become more and more important, and micro-assembly, as an important link in the field of micro-nano operation, has also received more and more attention. The quality will directly affect the quality of micro-assembly products, therefore, it is particularly important to adopt appropriate quality detection methods for small parts. Traditional manual detection methods have disadvantages such as low detection efficiency, high work intensity, and low precision. Other researchers have introduced computer microscopic vision into the system and combined some edge detection operators to detect defects in tiny parts through edge detection. This method can detect defects on the ...

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Problems solved by technology

Traditional manual detection methods have disadvantages such as low detection efficiency, high work intensity, and low precision. Other researchers have introduced computer microscopic vision into the system and combined some edge detection operators to detect defects in tiny parts through edge detection. This method can detect defects on the part surface quickly and accurately, but there are still some unnecessary detection errors. For example, when the edge detection threshold is not set properly, the edge of the part itself will be misdetected as a defect, or the part's edge will be mistakenly detected. The larger defect is mistaken for the edge of the part, or the noise of the image is mistaken for the defect. In addition, this detection method cannot distinguish the various defects detected

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