The application of dynamic photogrammetry to the dynamic monitoring of wind turbine blades is of great significance to optimize the design of wind power system and ensure the safe and reliable operation of the system. Automatic matching of image feature points is a key technology in dynamic photogrammetry system. The quality of matching method directly affects the final measurement accuracy. In the actual dynamic photogrammetry of wind turbine blades, the shimmy of the blades during operation will aggravate the non-planarity of the blades, which makes it more difficult to directly use the epipolar or homography matching technology to match the binocular image features. Therefore, a binocular image matching method based on pre-screening and local homography is proposed, which can be used in the measurement scene of wind turbine blades characterized by a smooth continuous surface. Firstly, the point images of the blade are obtained through the binocular photogrammetry system. For the point image captured by the left camera, the candidate matching point set of each point in the left image is filtered from the right point image through the epipolar constraint. Secondly, a local approximate plane region centered on this point is extracted for each point in the current point to be matched and its candidate matching points and all local approximate plane regions are combined accordingly. Then, for each group of approximate plane regions, the improved random sample consensus algorithm is used to estimate the corresponding optimal homography matrix model, Finally, the final point that matches the current point to be matched is determined in multiple candidate points through the local homography transformation and reasonable error threshold. This method has been successfully applied to the shimmy experiment of the wind turbine blade. The image point matching rate is not less than 96% and the matching accuracy is not less than 97%. The method can satisfy the requirements of actual wind turbine blade image matching for accuracy.