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To address the shortcomings of image segmentation algorithms with unsatisfactory segmentation effects, this paper improves the honey badger algorithm through the backward learning strategy and the Corsi variation factor, and then combines the two-dimensional OTSU for image threshold segmentation. First, the initialization of the honey badger population is improved by the reverse learning strategy to enhance the population diversity and distribution balance, thus improving the overall search ability of the algorithm; second, the Corsi variation factor is introduced to perturb the feasible solutions calculated by the algorithm, making it easier for the algorithm to jump out of the local optimum and enhancing the local search ability and convergence accuracy of the algorithm; finally, the two-dimensional OTSU optimized by MHBA is used for the segmentation of three Finally, the segmentation of the standard images is verified by the MHBA-optimized 2D OTSU. The experiment proves that the segmented images obtained by MHBA-OTSU algorithm have higher accuracy and more detailed effect, which verifies the effectiveness of the method.