RESUMO
Image matching is an important topic in image processing. Matching technology plays an important role in and is the basis for image understanding. In order to solve the shortcomings of slow image matching and low matching accuracy, a matching method based on improved genetic algorithm is proposed. The main improvement of the algorithm is the use of self-identifying crossover operators for crossover operations to avoid premature population maturity. According to the characteristics of the image data, new intersection and mutation operators are defined by the new coding method. The sampling method is used to initialize the population method, introduce an evolution strategy, reduce the number of iterations, and effectively reduce the amount of calculation. The experimental results show that the algorithm can guarantee the matching accuracy and that the calculation time is much shorter than that of the original algorithm. In addition, the image matching calculation time per frame of the algorithm is basically unchanged, which is convenient for engineering applications.