Improved fruit fuzzy clustering image segmentation algorithm based on visual saliency

Abstract: The vision location system of the picking robot, which is an important part of the robot, is mainly used to detect the spatial position of the fruit and provide the motion control system of the robot with position information. Extracting the fruit waited for picking in a complex background by selecting an appropriate image segmentation technology provides us with the full assurance to obtain the position information of the fruit. So, aiming at the problems that the traditional fuzzy clustering is sensitive to the initial clustering centers and has large amounts of calculation and image over-segmentation, combining with the picking robot visual characteristics, an improved fuzzy clustering segmentation algorithm based on the multi-scale visual saliency for fruit image was put forward in this paper. First, a color model of the litchi and citrus image was discussed respectively, their diagrams of the R-I color model was expatiated, the fruit color image was converted into gray image by selecting a R-I color model; the gray image was processed with different scale Gaussian filters and the image clustering segmentation space was formed by blending all the different scale Gaussian filtering images according to the visual saliency, effect chart of the multi-scale visual saliency image algorithm was given based on R-I, and the over-segmentation problem most of the fruit image fuzzy clustering segmentation algorithms was solved. Second, the high dimensional clustering segmentation space based on pixels was changed into the low dimensional clustering segmentation space based on the histogram and the gray level by using the histogram method and the specific steps of image segmentation algorithm was given; the calculation of the fuzzy clustering image segmentation algorithm was greatly decreased and the fuzzy clustering image segmentation speed was improved. Furthermore, in the light of the problems that the fuzzy clustering algorithm easily fell into the local extreme value, the clustering center was optimized with the particle swarm algorithm based on simulated annealing, and the image clustering segmentation performance was improved. At the same time, the cooling strategy and state acceptance probability function of the particle swarm algorithm based on simulated annealing was nonlinearly reformed. Finally, the fuzzy clustering image segmentation algorithm based on multi-scale visual saliency of this paper was tested with 50 randomly selected images each of the 100 ripe litchi images and 100 ripe citrus images, and the contrast effect charts of the traditional and improved fruit image segmentation algorithms were given. The experimental results showed that: for the ripe litchi and citrus image, the average fruit segmentation rate of this method was 95.56% and 93.68%, and the average running time was 0.724 s and 0.790 s. The algorithm could meet the requirement of fruit image segmentation and real-time operation of the picking robot in the real picking activities; It has also provided a new basis algorithm for the image segmentation and its real-time research, and offered testing data for the vision accurate location of the picking robot.