Research On Key Algorithm Of Image Processing System Of Fruit Picking Robot

China is a large country where a lot of fruits grow in.In order to alleviate shortage of the labour force and raise labor productivity, machine vision technology is applied in fruit atomatic picking system. Generally, for the fuit trees grow in the non-structural environment, the image captured by computer vision system of picking robot is composed of sky image, branches image, leaves image, soil image, fruits image and et al. The varieties of growth morphology of fruits and the changeable sunlight determin that buiding a vision system is a complex system engineering.Regarding images of fuit trees taken in natural scene as the research object, with the help of technology of image processing,computer vision and artificial intelligence, the key technology of vision system of fruit picking robot was studied in this paper, the main work and reserch results were as following:l)Color Image Segmentation based on improved K-means algorithmFirstly, after making an intensive study on the color spaces,21color characters were selected from RGB,HIS,YcgCr,YcbCr and CIE five color spaces. The combination of color characters (H,Cr(YCgCr), Cr(YCbCr), R-G,2R-G,Cb-Cr) group which had maximum recognition rate and minimum recognition error rate were found based on BP neural network, and the combination of color characters was used as the feature vector to segment the fruit image; Secondly, images in recognition sample space were segmented by using the improved k-means algorithm based on variance coefficient weighting method. The overall results showed that the proposed method could segment the ripe peach image under natural sun light validly, it could also segment the images taken opposite the sun, the recognition accurate rate could obtain89.5%. The method proposed in this thesis which combined the advantage of several color spaces,could raise the image segmentation efficiency and identification accuracy.It could also overcome the defects of trandional euclid distance in clustering algorithm.2)Two-step method based on texture and color characters to separate the fruits from the background acculatelyRegarding the fruit images captured in natural scene as the research subject, the fruit recognition methods basedon texture and color charcter were studied, and two-step method based on texture and color characters to separate color image was put forward. Firstly, image was divided into blocks whose size were16×16and8×8, energy and contrast based on Gray-level Co-occurrence Matrix((0°,45°,90°,135°) were calculated in the two kinds of block. Four kinds of texture character:Enger of horizontal detail image, contrast of horizontal detail image, enger of vertical detail image and contrast of vertical detail image were obtained after the image was first decomposed with Haar wavelet in these two kinds of block. After the best texture characters:con and conCvl of16×16block were selected by BP network, the position of fruit in image was located approximately by using the trained BP neural network to segment the image. Then the image was segmented again by use of the color characters:H and R-G. The results showed that two-step method could get good segmentation results for images opposite the sun or front the sun.3) Location and detection for single quasi-circular fruits based on improved hough transformIn order to calculate accurate centric coordinates and radius of quasi-circular fruit rapidly, a kind of detection method for quasi-circular fruits based on improved circular randomized Hough transform was proposed. After the object was segmented from background with2R-G, the thinning algorithm was used to extract one-pixel fruit contour, from which the edge character points were abstracted. Then the edge character points were grouped according to their average tangent directions, with which the circular RHT algorithm was improved. Last, the centric coordinates and radius of quasi-circular fruits were calculated with the optimized circular RHT algorithm. The overall results showed that the proposed method could detect the quasi-circular fruits rapidly and accurately, it could also recover the shape of part-covered fruit satisfactorily.4)Location and detection for overlapped fruits based on searching concave spotsIn order to calculate centric coordinates and radius of quasi-circular ripe fruits whose growing state was approached or overlapped, a kind of fast location and defection method for fruits object based on searching concave spots was proposed. After the object was segmented from background with hue according to statistical law, the freeman chain code algorithm was used to extract one-pixel fruit contour, from which the edge character points were abstracted. Then the edge concave spots which were found by direction coding difference of N points, were divided into several concave spot groups, and the segmentation concave spots were located by the threshhold. Last, the centric coordinates and radius of several overlapped peaches were calculated based on optimized circular Hough transform. The research results show that the proposed method can detect the overlapped ripe fruits accurately and rapidly.5)Location and detection for branches based on gradient phase grouping A new method of Hough transform based on gradients phase grouping for branches detection was proposed to locate the position of branches of fruit trees accurately and rapidly. After the gradient phase of edge points were calculated by using the squared gradient method, the histogram of gradients’s direction were calculated. From the histogram, several peak values were found by threshold T. Then edge points were grouped according to their gradent phases, and points in each group almost had the same gradient’s direction. Last, an improved two-points Hough transform was applied to edge points in each group to calculate the parameters of lines, and the gradent’s direction of every group was used to affirm the correctness of the parameters. The research results showed that the proposed had the merits of high speed, low error and strong robustness, and could locate the branches of fruit trees accurately and fastly,it could also detect part-covered branches satisfactorily.6) Study on methods to estimate the growing attitude of single appleIn order to avoid damaging apples and branches caused by manipulator during the picking operation, for the absence of attitude information, an apple’s attitude estimation method was put forward. After the apple object was segmented from background with the two-step algorithm based on the characters of color and texture, the freeman chain code algorithm was used to extract one-pixel fruit contour. Then least distance method, least slope variance method and three collinear points method were given,and the recognition rates of three methods were compared. Lastly, for the purpose of improving recognition rate, decision method based on fusion of four methods was proposed. The research results showed that the recognition rates by use of four methods were higher than using any of methods seperatly, and the recognition rates could reach90%.