Charge Couple Device (CCD) Photoelectric Signal Data Acquisition and Its Application in the Machine Vision of Artificial Intelligence

Abstract

The development of Charge Couple Device (CCD) technology is particularly rapid in the fields of image sensors and non-contact measurement. In this study, a data acquisition device applied to CCD photoelectric detection system is designed. Among them, the design of the Differential Amplification (DA) module, Analog-to-Digital Converter (ADC) module, First In First Out (FIFO) cache module, and Complex Programmable Logic Device (CPLD) module in this device are emphasized. The ADC circuit in the ADC module converts two 4 MHz analog photoelectric signals generated by the CCD sensor at a frequency of 8 MHz, and then outputs 12-bit digital signals. The collected photoelectric signal is used to detect the damage to the surface of ancient buildings with the machine vision technology of artificial intelligence (AI). In the test, the DA circuit can adjust the voltage range of two photoelectric analog signals output by CCD to a predetermined range (1.5 V∼2.0 V). In the ADC circuit test, there is no data in the FIFO when there is no input conversion, and the converted data will be stored in the internal FIFO during the conversion clock period. Based on machine vision technology, surface damage types of ancient buildings are defined, namely spalling, cracks, and disruption, and surface image samples are generated from collected signals. The samples are trained using the convolutional neural network, and the classifier is generated. The test reveals that the designed photoelectric signal acquisition device and AI machine vision technology can accurately classify the surface damage of ancient buildings.