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.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.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.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.