针对传统聚类算法处理桥梁裂缝提取问题时，由于阴影和光线不均匀导致识别精度低、易丢失特征信息等问题，提出了一种基于混合秃鹰-天鹰优化器（Hybrid Bald Eagle- Aquila Optimizer，HBAO）交叉迭代改进模糊C–均值（Fuzzy C-Means，FCM）聚类的桥梁裂缝提取方法。首先，引入耦合混沌映射初始化，并融合折射学习思路，增加了种群多样性；其次，为增强秃鹰算法的全局搜索阶段的性能，将该阶段替换为天鹰优化的扩展和缩小搜索策略，显著提升了秃鹰算法的收敛趋势和全局寻优能力，提高了FCM的寻找最优聚类中心的成功率；然后，利用HBAO与加权熵法对FCM聚类算法进行联合优化，提高鲁棒性的同时还增强了搜索精度，获得较好的聚类结果；最后，在UCI标准数据集上与6种对比算法进行聚类性能评估实验，验证了本文算法综合性能优越。进一步地，将算法在4种不同裂缝形态上进行测试。实验结果表明，相比于其他同类算法，所提算法的裂缝提取效果最优。

In order to solve the problem of bridge crack extraction by traditional clustering algorithm, the recognition accuracy is low and the feature information is easy to be lost due to the uneven shadow and light, a method of bridge crack extraction based on hybrid bald eagle-aquila optimizer (HBAO) cross iteration was proposed to improve Fuzzy C-Means (FCM) clustering. Firstly, coupled chaotic mapping initialization was introduced, and refraction learning was integrated to increase population diversity. Secondly, in order to enhance the performance of the global search phase of the bald eagle search (BES) algorithm, this phase was replaced with the expanded and narrowed search strategy of the BES optimization, which significantly improved the convergence trend and global search ability of BES, and improved the success rate of finding the optimal clustering center of FCM. Then, HBAO and weighted entropy method were used to optimize FCM clustering algorithm, which improved the robustness and enhanced the search accuracy, and obtained better clustering results. Finally, the clustering performance evaluation experiment is carried out on UCI standard data set with 6 comparison algorithms, and the comprehensive performance of the proposed algorithm is verified. Furthermore, the proposed algorithm is tested on 4 different fracture patterns. Experimental results show that compared with other algorithms, the proposed algorithm has the best extraction effect.