# Deep Learning-Based Occlusion Handling of Overlapped Plants for Robotic Grasping
## 2 Related Work  
Recently, along with many advances in DL, the problem of occlusion in scenes for the instance segmentation tasks has been studied [12; 13; 14; 15; 16]. Instance segmentation, a computer vision task for detecting and localizing instances of each object class in an image, represents one of the most challenging problems and encompasses a variety of aspects that are attracting increasing research interest in the computer vision community. Instance segmentation of occluded objects is difficult due to the lack of a complete shape of the object, and only a few methods, such as those presented in [13; 14], consider both the complex shape and occlusions of the objects. The method presented in [17] is a CNN-based method that works with overlapping objects in 2D images and simultaneously segments and classifies objects. Sheared 2D and 3D masks of the overlapping objects are encoded in a volumetric image, and this method performs 3D object segmentation. The main contribution of this network is its ability to generate class-specific instance masks of overlapping biological objects. This method works with 2D images of translucent objects, which makes it easier for the network to lift the label space from 2D to 3D. Presented results in [18] showed that this method achieved high accuracy in overlapping and cluttered situations. However, this method does not suit the solution of the problem of overlapped plants, considered in this paper, as plant objects are not translucent. In [19], a three-layer model is proposed to jointly represent hypotheses, voting elements, instance labels, and their connections for plant imaging analysis. This method deals with partial occlusions as well. The first step in this method is the detection of object centers by Hough voting, and then the instance is segmented around the detected focused point. With updating the Hough votes, all the assignments and weights are updated, and the process is repeated until a stop criterion is met. The possible application of this method for the segmentation of plant cuttings has some limitations. One  
Figure 1: Pipeline of the proposed system for detection of robotic grasps of plant cuttings on a flat surface such as a conveyor belt. of them is the inability to correctly detect plant stems, as the framework of this method is designed for the specific application of plant leaf segmentation. In addition, in cases of heavy occlusion or small leaves, plants' leaves cannot be accurately segmented. Some existing methods for handling occlusion are dependent on the objects' bounding boxes proposals problem. For example, the method presented in [12] is dependent on the bounding boxes proposal and makes use of image synthesis of objects with occlusion, which is an extension of methods presented in [18; 20]. The authors of [12] use a novel relook architecture, which makes use of instance density to segment multi-class masks, extending so the methods already implemented in [21; 22]. In addition to segmenting the visible objects, it also generates the invisible parts of the objects. The method presented in [23] is another bounding box-dependent method that performs segmentation and generates the invisible parts of the objects. As this method jointly segments and generates the invisible object's parts, it gets more information about the dependencies of the objects with each other and their occluded regions, their shapes, and appearances. Authors of [24] present a method using oriented boxes instead of axis-aligned boxes for instance segmentation. This method shows that oriented boxes achieve better results and improve the mask predictions especially when the objects are diagonally aligned, overlapping, or touching each other. However, Refs. [12; 23; 24] considered simple shape objects such as screws, pill bags, and furniture, which are not complex shape objects such as the plant cuttings in our dataset, and therefore the corresponding methods are not suitable for the segmentation problem considered in this paper.