Improved Flower Pollination Algorithm And Its Application In Industrial Image Processing

In recent years,image processing technology has been widely used in industrial fields such as defect detection,workpiece size measurement,and product auxiliary assembly.Image enhancement and image segmentation are the core links of image processing,and are also hot spots in academic research.Therefore,this paper proposes an improved flower pollination algorithm and applies it to the field of industrial image enhancement and segmentation.First of all,in view of the slow convergence rate of the basic flower pollination algorithm,this paper proposes an improved flower pollination optimization algorithm.Firstly,linear weights are introduced to gradually enhance the local search ability of the algorithm.Secondly,a high-quality random walk strategy is proposed to further improve the search ability of the flower pollination optimization algorithm.Finally,the improved flower pollination algorithm is used for function optimization and compared with genetic algorithm,gravitational particle swarm optimization algorithm and basic flower pollination algorithm.Simulation experiment results show that the algorithm proposed in this paper is excellent in convergence accuracy and convergence speed Used in other algorithms.Secondly,the improved flower pollination algorithm is applied to industrial image enhancement.Using the excellent search ability of the algorithm,the optimal transformation parameters are found,and then the image is enhanced.Compared with the other three algorithms,the effectiveness of the improved flower pollination algorithm for industrial image enhancement is proved.Finally,this paper proposes to apply the improved flower pollination algorithm to the segmentation process of industrial images.The maximum inter-class method(Otsu)and maximum entropy method(Kapur 's entropy)are selected as fitness functions.The experimental results show that the industrial image segmentation method based on the improved flower pollination algorithm has high segmentation accuracy and robustness.