The recognition and location of overlapping or adjacent grape clusters in vineyard is one of the difficulties of grape picking robot vision system. In order to locate the grape clusters accurately, a method for targets detection and extraction in two overlapping and adjacent grape clusters was proposed based on image contour analysis. Firstly, the H color component images that can well distinguish the summer black grape clusters from the background were extracted from the HSV color space, the grape clusters in the extracted images were segmented by using the improved K-means clustering method, and subsequently the noises in the segmented images were eliminated by using morphological operations. Secondly, the edges of grape clusters were extracted, and the midpoint of the line crossed the extreme points on the left and right edge of grape clusters was calculated out. Thirdly, midpoint was taken as the original point, and a geometry calculation model for solving the dividing line between two grape clusters was built after analyzing the contour characteristics. The two intersection points of the adjacent grape clusters’edges were computed by using the minimum distance constraint between the original point and the specified edges. Finally, the dividing line of two grape clusters was obtained by connecting the two intersection points, and the two grape clusters were extracted separately. To verify the robust of the proposed method, totally 27 vineyard images with two overlapping and adjacent grape clusters were tested, and the results showed that the grape clusters in 24 images were correctly identified and extracted. The success rate reached up to 88.89%, and the accuracy of the extracted pixel region was from 87.63% to 96.12%. The elapsed time of the developed algorithm was 0.59 ~ 0.68s. Moreover, the developed algorithm was transplanted to the selfdeveloped harvesting robot, and the running results showed that the proposed method could be used to localize two overlapping and adjacent grape clusters.