# Detection and Localization of Overlapped Fruits Application in an Apple Harvesting Robot
## 1 Introduction  
In the related research on apple orchards' yield measurements or mechanical harvesting of apples [1; 2; 3], this is the key to recognizing and localizing apple targets. The yield measurement and harvesting robot system's real-time and reliability mainly depends on the speed and accuracy of the vision system. For the identification and location of a target fruit, a lot of research has been carried out, and gratifying achievements have been made [4; 5; 6; 7]. However, they are mainly devoted to the research of static or non-vibrating single shadow fruits. The speed and accuracy of this type of target recognitionand positioning have reached a higher level. Under natural conditions, due to the influence of the apple's growth posture and camera position, the captured apple images have various forms, such as overlapped apples, shaded by leaves, shaded by stems, and so on. Especially for overlapped apples, it is difficult to pinpoint them. They will affect the localization and recognition accuracy, which in turn will affect the harvesting efficiency of the apple harvesting robot or the production accuracy of the orchard yield measurement. At present, the research on overlapped fruit recognition algorithms mainly focuses on slightly overlapped fruits, which has achieved good results [8; 9; 10]. However, there are serious overlaps or shadows in the fruit, which is a great challenge to the accuracy of recognition or positioning. However, many scholars have been attracted by research into overlapped target recognition. For example, in the field of biomedicine, [PERSON] [11] proposed a method which combines watershed algorithms and gradient range overlapped chromosomes segmentation algorithms to analyze some chromosomes that are occluded from each other. The recognition rate of overlapped chromosome can reach 80.4%. [PERSON] [12] has proposed a cell adhesion cell image segmentation and reconstruction algorithm based on mathematical morphology. It is used for automatic analysis of molecular images. These two cases are the recognition of overlapped targets. They can provide valuable inspiration for recognizing overlapped apples. At the same time, some research has been carried out on the overlapped targets of other fruits. For example, according to a statistical method, [PERSON] [13] used hue separation background and a Freeman chain code algorithm to extract one-pixel fruit outline, and took use of concave points search to optimize the circular Hough transform to locate the central coordinates and radii of several overlapped peaches. [PERSON] [14] used a directional gradient histogram (HOG) operator based on support vector machine (SVM) to recognize overlapped strawberries with an accuracy of 87%. In both cases, overlapped peaches or strawberries could be recognized. Some recognition algorithms will be converted to recognize overlapped apples. These methods provide new ideas to solve the recognition problem of overlapped apples. In addition, some achievements in other fields can also provide innovative ideas for our future research. There is no doubt that some scholars have put a lot of effort into identifying overlapping apples and have made some achievements. For example, [PERSON] [15] studied fast tracking and recognition of overlapping fruits. For the fitting and prediction of robot paths, a fast-normalized cross-correlation matching method was used, and the recognition time was reduced by 28.1%. [PERSON] [16] used back propagation (BP) neural networks for segmentation and fitted the center radius of the circle according to the change in contour curvature. [PERSON] [17] first performed color difference segmentation on the image, then performed some morphological processing on the apple to obtain the contour, and finally fitted the circle by least squares analysis. [PERSON] [18] proposed a combination method of Snake model and corner detection to segment overlapped targets which greatly reduced the segmentation error of overlapped apples.