Abstract:

In order to solve the problem of huge computation and high time cost in subpixel registration resolution of digital image correlation, we propose a new improved PSO for subpixel registration of the respective flight velocities. The flying velocity and range of particles which are subdivided at two directions x and y, can adaptively adjust according to the deformation degree of each interest point so as to improve their displacement solution quality. In addition, the reliable mutual learning mechanism is introduced and the historical information of the previous feature points is fully utilized, which helps to reduce the number of iterations and enhance algorithm efficiency. Compared with the NewtonRaphson and the NRPSO, the proposed method has higher accuracy, and the feasibility and availability are verified. Particularly, the superiority of time cost is more distinct when dealing with a large number of interest points.

Key words: digital image correlation;subpixel registration resolution;mutual learning adaptive particle swarm optimization;NewtonRaphson algorithm