Super-cavitation contour extraction method based on multi-scale space

Abstract

Super-cavitation morphological parameter information of the cross-media projectile reflects high-speed projectiles motion characteristics. Accurate measurement of super-cavitation morphological parameters is the basis for high-speed projectile structural design and optimization, becoming a research hotspot of the underwater ballistic testing field. With the development of the high-speed photography technology, recording the evolution process of projectile cavitation morphologies by high-speed cameras and acquiring super-cavitation morphological information through image processing methods have become one of the main research directions. Aiming at reducing the large workload of manual interpretation from super-cavitation images, a multi-scale space-based automatic contour extraction algorithm for supercavitation images captured by the array cross-media high-speed imaging system is introduced in this paper. The Canny edge detection method is applied to obtain the binary edge image with coarse extraction super-cavitation edge after image pre-processing. The super-cavitation contour point set is searched in the binary edge image and small gaps between adjacent endpoints are filled at the same time. The contour extraction algorithm based on the multi-space curvature characteristics is presented to calculate the curvature value of each contour point in the high-scale space, and the curvature feature points are determined by the threshold judgment approach. The contour feature points extracted in the high-scale space are located in the low-scale space precisely, and the super-cavitation contour is extracted. Experimental results show that, the proposed method can reduce the workload from manual interpretation while improve the accuracy of contour positioning, which can provide basic data support for the cross-media high-speed projectile super-cavitation morphological parameter measurement.