To address the issues of high operation and maintenance costs, low detection efficiency, and remaining limitations in damage identification and localization of current wind turbine blade health detection methods. A non-contact measurement technology based on binocular vision and a blade fault diagnosis method have been proposed. First, to address the challenge of large-field-of-view camera calibration, the inverse depth-based Bundle Adjustment (BA) algorithm was used in nonlinearly optimizing the external parameters of the binocular vision system, thereby improving the measurement accuracy for large field-of-view applications. The study on damage detection in a turbine blade was also explored based on experimental deflection measurements. By fitting the rotation plane of the turbine blade, the normal deformation of the rotary blade was extracted through the coordinate transformation. And the location of the damage in the blade was then determined by comparing the difference in peak-to-peak magnitudes between blades with and without cracks. Finally, an experimental wind turbine model was built with blade length of 2.15 m. The 3D displacement distribution of the rotating blade was characterized, and the prefabricated cracks in the blade were also detected. The measurement precision is within an error margin ranging from -0.05 m to 0.05 m. The feasibility of the proposed methods is preliminarily verified with the satisfactory measurement results.