面向深空探测图像分割的群智能混合优化算法

Group Intelligent Hybrid Optimization Algorithm for Image Segmentation of Deep Space Exploration

深空探测任务中,探测器需要在复杂的地形区域着陆,因此在轨障碍的快速检测至关重要,而图像分割是在轨检测的关键过程之一。鉴于此,提出一种基于粒子群和灰狼混合优化的多级阈值图像分割算法。在寻优过程中,所提算法在考虑图像能量分布的情况下,针对不同场景通过改变种群初始条件来自定义阈值级数。在位置更新过程中,所提算法增加扰动算子来扩大全局搜索的范围,引入动态权重来平衡群体的全局搜索能力与局部搜索能力,从而提高寻优的速度和精度,完成图像分割。实验结果表明,相较于传统的群智能算法,所提算法表现出较好的搜索能力,在处理灰度直方图不呈现双峰的复杂图像问题上有明显改善。

In the deep space exploration missions,the detector needs to land in complex terrain areas.Therefore,the rapid detection of on-orbit obstacles is very important,and image segmentation is one of the key processes of on-orbit detection.In view of this,a multi-level threshold image segmentation algorithm based on particle swarm and gray wolf hybrid optimization is proposed.In the optimization process,the proposed algorithm defines the threshold series for different scenes by changing the initial population conditions considering the image energy distribution.In the process of location update,the proposed algorithm increases the perturbation operators to expand the scope of global search,and introduces dynamic weights to balance the global search ability and local search ability of the group,thereby improving the speed and accuracy of optimization and completing image segmentation.The experimental results show that compared with the traditional swarm intelligence algorithm,the proposed algorithm shows better search ability,and it has obvious improvement in dealing with the problem of complex images where the gray histogram does not show bimodal peaks.