Fruit image segmentation based on evolutionary algorithm

Abstract: An improved evolutionary algorithm based on queen mating combined with elite and truncated choices by stages was proposed for fruit image segmentation, which was appropriate for the demand of the picking robot for real-time image and adaptive processing algorithms. The 8 bit binary code was used to correspond with the gray value of the fruit image in the improved evolutionary algorithm. The number of the initial population was set to 12 in the phase of the population initialized and the corresponding individual values, which ranged between 0 and 255, were generated by the random function. The twelve random numbers were the initial values of the evolutionary algorithm. Then an improved Otsu algorithm formula was selected as the fitness function. In the selection phase, the iterative process was divided into before stage, middle stage, and after stage, which were respectively used by queen mating algorithm, elitist choices strategy, and truncated choices strategy to select the fitness value. In the first stage, the individuals were produced by a random function and then the best individual (queen) of the evolutionary algorithm was hybridized with the rest of the individuals (including the randomly generated individuals) to generate new individuals. Finally, the individuals with the smallest fitness values were replaced by the new individuals. In the second stage, the elitist choices strategy was used and the individual with the smallest fitness value in the current generation was replaced by the individual with the highest fitness value in the previous generation. In the third stage, the truncated choices strategy was used and the last half of the individuals with the smallest fitness value in the current generation was replaced by the same number of individuals with the highest fitness value in the previous generation. This not only ensures the diversity of the population, but also overcomes the disadvantage of local optimized and too fast a convergence of the traditional evolutionary algorithm. In the crossover phase, it uses a single-point crossover method. In the mutation phase, the selected mutation probability was 0.2, which was obtained by comparing the results of different experiments. In the termination phase, the termination condition of the evolutionary algorithm in this paper was that the number of the current iteration had reached the maximum number set by the user in advance. The experimental results showed that the proposed fruit image evolutionary segmentation algorithm was obviously superior to the traditional evolutionary algorithm, and was better in terms of stability, segmentation effect, running speed, etc, and the segmentation threshold value was stabilized within three pixels. Compared with the Otsu segmentation algorithm, K-means clustering segmentation algorithm, fuzzy C-means clustering segmentation algorithm, and Bayesian classification segmentation algorithm, the fruit image evolutionary segmentation algorithm was the best segmentation effect and had the least run time. The average run time of the evolutionary algorithm was 0.08735 seconds, which was less than the other 4 algorithms. The evolutionary segmentation algorithm could be used for citrus, litchi, apple, and other fruits image segmentation and so the algorithm has certain universal utility. The algorithm was achieved by the demand of vision real-time positioning of the fruit picking robot and had provided a new basis algorithm for the image segmentation and its real-time research.