Abstract: Aiming at the problem that the two-dimensional maximum inter-class variance threshold method (2D-Otsu) had weak anti-noise and long calculation time,a seeker optimization algorithm based on random weight and asynchronous value factor is proposed.The algorithm is applied to the image segmentation of rail defects in 2D-Otsu.The random weight is used to speed up the convergence speed of the algorithm,and the asynchronous value factor is used to improve the algorithm's search ability,which is conducive to global convergence to the optimal value.Through the test function analysis,the WFSOA algorithm can converge quickly,the precision value of the optimization value is high,the convergence time is small,and the algorithm has good stability.In the image segmentation of rail defects,the 2D-Otsu trace function is used as the objective function of WFSOA.The experimental results show that the image detection has high real-time performance,the segmentation result of the rail defects is clear,and the false detection rate and missed detection rate of the rail defects are effectively reduced.Time is only 2% of the 2D-Otsu algorithm,which meets the needs of actual engineering.

Key words: 2D-Otsu, Image segmentation, Improved seeker optimization algorithm, Rail defect, Threshold