Abstract
In the daily work of technical defense of universities, many optical devices need to be managed, and there are many components that are easily damaged in these devices. Therefore, timely detection of component damage in optical equipment is of great significance for reducing equipment losses and improving work efficiency. The traditional detection method is to use professional equipment for testing after removing the equipment. In this way, for the technical defense work of universities, the real-time online of the equipment cannot be guaranteed and the use cost of the equipment will increase. In the field of computer vision, deep learning and convolutional neural network technology have begun to play an important role with the wave of artificial intelligence. Therefore, this subject attempts to apply this technology to the damage detection of technical defense equipment in universities, thereby improving work efficiency and reducing equipment loss.
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