Non-destructive infrared detection technology for wings based on frequent subgraph mining algorithm

In response to the problem of poor accuracy in wing non-destructive testing, a wing non-destructive infrared testing technology based on frequent subgraph mining algorithm is proposed. This technology combines infrared imaging technology with frequent subgraph mining algorithms in graph theory. By converting infrared images into graph structures and using algorithms to identify frequently occurring subgraph patterns in the images, convolutional neural networks and decision tree classifiers are applied to achieve efficient detection of wing defects. The experimental results show that this technology can handle the complex and diverse structures and materials of wings, and identify different types of defects by accurately matching subgraph patterns, thereby improving the recall rate of detection. In addition, with the continuous development and optimization of technology, as well as the support of big data and artificial intelligence, this technology can further improve detection performance and efficiency, providing more reliable and accurate support for safety monitoring in the aviation industry.