Abstract
Computer vision-based model attitude and deformation measurement is essential in wind tunnel testing, offering benefits such as non-contact operation, automation, visualization, flexible configuration, and high near-field accuracy. However, the specific conditions of wind tunnel environments impose certain limitations on the practical applications of visual measurement techniques. In response to the intelligent demands of wind tunnel tests, this paper summarizes applications for model attitude and deformation measurement in wind tunnels. Over time, visual measurement techniques in wind tunnels have advanced from simple non-contact methods to real-time attitude and deformation assessments. Current trends highlight a shift towards multi-dimensional data fusion and deep learning techniques, which harness the complementary strengths of various technologies. This integration enables the simultaneous measurement of diverse data, significantly improving the efficiency of wind tunnel tests and achieving higher measurement accuracy.