# Accurate Instance-Based Segmentation for Boundary Detection in Robot Grasping Application
## 1 Introduction  
In the robot grasping field, the accuracy of instance segmentation plays an important role. It decides the position controlling, the open angle of the robot's arm. Moreover, it is the first step of finding object pose estimation, which is necessary for proposing a possible grasping plan [1, 2, 3]. If the detailed information at the edge region is overlooked, it will cause the negative situation that the arm of the robot may collide with the objects. Thus, the accuracy of the segmentation results is crucial in this field.  
The advent of Convolution Neural Network (CNNs) in the 1990s opened a strong step forward for handling computer vision tasks in the future. In 2016, the Region-based Convolutional Neutral Network method (R-CNN) [4] was proposed and reached excellent object detection accuracy by using a deep CNN to classify object proposals. Then, Fast R-CNN [5] and Faster R-CNN [6] introduced later greatly improved both the accuracy of target detection and time processing. For object segmentation, based on the advantages of previous detection principles, Mask R-CNN [7] upgraded object detection and segmentation in image exceptionally. Nonetheless, the state-of-the-art Mask R-CNN did not consistently make accurate predictions, especially when objects were placed obscure to each other or there was a lack of light. Solving these problems, a scoring path was added to the mask head to increase the supervision of the mask in MS R-CNN [8]. Mask Refined R-CNN (MR R-CNN) [9] substantially improved the accuracy segmentation in the entire object by combining feature layers, which focus on the global and detailed information.  
Although many works based on 2D data have achieved high accuracy in segmentation, they all have the same limitations. The image brightness and object obstacle reduce the performance of these works, and the pixels at the object edge are usually misclassified. Though 3D segmentation algorithms can easily take all the pixels of objects based on spatial location of object, it is impossible to obtain a good segmentation result without considering the features of 2D image. In addition, the state-of-the-art 3D segmentation model works well at part segmentation, scene semantic parsing, but not for the entire object. Motivated by these disadvantages and advantages, we propose a method that integrates Mask R-CNN and 3D segmentation algorithms for improving the accurate instance segmentation in the object edge region. The main novelty of our works can be summarized as follows:  
* Adding a 3D process branch to conserve full pixels of objects by Difference of Normals-Based Segmentation [10]. Dealing with multiple objects and occlusion, which leads to the misclassification in Mask R-CNN.
* Distinguishing the interesting edge regions based on the relationship with the original masks.
* Continuously considering the spatial distance between edge regions and each mask region to categorize the edge region by Euclidean Cluster extraction [11].  
The idea of combining 2D neural network model and 3D algorithms will open many opportunities in the future. With this proposed method, robots have full and exact awareness of object region, object localization and accomplish the purpose of grasping better. In addition, it can be applied to many different segmentation models to improve their performance.  
This paper is organized in the following context: Section 2 shows the research related to our purpose. Mask R-CNN pipeline and the combination of Mask R-CNN and 3D algorithms are presented in Section 3. Section 4 shows the experiments and the result of our approach. The discussion, conclusion and further work are detailed in the last two sections.