目的 传统广义Hough变换可以在平移、旋转、缩放、局部遮挡等情况下，对任意目标进行定位，但是存在定位速度较慢、存储空间较大、累加器空间离散化等缺点。因此提出了基于全局自适应人工鱼群的广义Hough变换算法，对目标进行更快地定位。方法 根据目标形状的极坐标信息建立精简R表，去除梯度信息，降低计算复杂度，同时提高目标模型的鲁棒性；然后，根据精简R表计算待测目标模型函数值，作为人工鱼的适应度值，人工鱼群采用自适应的感知范围和步长，通过不断交互并协调行为，在连续的多维累加器空间中启发式地搜索最优目标模型参数，从而标定出目标的准确位置。结果 实验结果表明，该算法只需要常量级的存储空间开销，并且与广义Hough变换算法相比速度提高了90%以上，较大地减少了空间和时间开销，也提高了目标的定位精度。结论 新的累加器空间搜索策略，能够更快速准确地定位目标，特别是在复杂背景下对复杂目标定位更为明显。

Objective Traditional generalized Hough Transform in translation,rotation,scaling,partial occlusion and other circumstances,can locate any target, However, the slow positioning speed, the large storage space, the discrete accumulator space, and other problems hinder the usability of this method.Method Therefore, an improved generalized Hough transform based on a global adaptive artificial fish swarm algorithm is proposed to locate targets more quickly.According to the polar coordinates of the target shape information a reduced R-Table is established, which removes the gradient information to reduce the computational complexity and improve the robustness of the target model.Then we use a reduced R-table to calculate the value of the candidate target model as an artificial fish fitness value.And the artificial fish uses adaptive vision and step as well as constantly interaction and coordinate behavior to search the optimal target model parameters in the continuous multi-dimensional accumulator space heuristically which demarcates the exact location of the target.Result Experimental results show that,the algorithm requires only a constant level of storage space cost.The speed is improved more than 90% compared with the generalized Hough Transform algorithm.The algorithm not only greatly reduces the cost of space and time,but also improves the positioning accuracy of the target.Conclusion We propose a new search strategy in the accumulator space,which can be more quickly and accurately to locate the target,especially the complex target in complicated backgrounds.