on object detection. [PERSON] [4] used a deep neural network to learn the method of closed-loop controller for machine sorting, and obtained the accurate grasping posture of the object by training the convolutional neural network to learn the distance function, with which a better grasping effect was achieved. [PERSON] [5] detected and classified the target objects in the image through a deep learning-based object detection algorithm, and then performed the grasping position detection. The robot grasping position was determined by performing a box searching on each image. In similar work, many scholars have carried out special modeling trained for grasping rectangle detection. [PERSON] [6] proposed a multi-modal detection model based on deep learning. Color and depth information are used to detect objects. After being trained and tested on the Cornell dataset, this method achieved an accuracy of 89.21%, while ensuring the real-time performance of the system. Similarly, [PERSON] [7] proposed a fully convolutional visual grasping detection network based on directed anchor boxes, which realizes real-time detection of grasping. The aforementioned methods not only achieve high accuracy and a high recall rate, but also ensure the real-time performance of the system to a certain extent. However, the mentioned methods are quite difficult to accurately detect the targets when objects are heavily stacked since the semantic understanding of complex scenes is lacking. Thus, they are quite difficult to accurately detect when objects are heavily stacked. Recently, some other methods for robot grasping were proposed based on 3D object detection [8; 9; 10; 11]. The grasping pose and position of the object can be determined effectively, though the real-time performance cannot be guaranteed. In addition for objects whose shape and texture features are not obvious enough, the estimation of the optimal grasping pose is difficult to train effectively. In another type of research, segmentation-based deep learning model is used to detect grasping regions. [PERSON] [12] proposed a robotic sorting system for known items and novel items in complex environments, which won the first place in the 2017 Amazon Picking Competition. The system uses multiple installed RGB-D cameras as data sources. Additionally, it utilizes deep neural networks based on ResNet-101 [13] and FCN [14] to segment pixel-wise grasping candidate regions. Then, the regression scores of the candidate regions are calculated. At last, a series of optimization strategies is proposed to select the best grasping point. The system can achieve 96.7% gripper grasping accuracy and 92.4% suction grasping accuracy, and can sort new items well. Similarly, [PERSON] [15] employs two connected deep neural networks, one as an object detector and one for detection of functional regions of objects. It was proved that combining an object detector and using CRF [16] for post-optimization can achieve a high detection effect. It was experimentally verified on a full-scale humanoid robot. On this basis, [PERSON] [17] proposed AffordanceNet, which borrows the idea of the famous instance segmentation algorithm Mask R-CNN [18]. An end-to-end network structure is used to implement object classification and the segmentation of functional areas. This type of system more directly determines the grasping point or the segmentation of the candidate grasping area. As these segmentation algorithms mostly adopt a heavy network structure, they often need to balance the accuracy and real-time performance in complex scenes. For the scene of express parcel sorting, [PERSON] [19] proposed a robot sorting method based on a deep neural network in complex scenes, extracting more detailed candidate regions by fusing shallow feature multi-layer and final feature maps. A keypoint-based cascaded optimal sorting position detection network was proposed to detect parcels in real time. The model based on key point detection in this study was designed in three dimensions.