# ODGNet: Robotic Grasp Detection Network Based on Omni-Dimensional Dynamic Convolution
## 1 Introduction  
With the development of artificial intelligence technology, the potential applications of robotic grasping have shifted from traditional industrial environments to a variety of unstructured scenarios, such as medical assistance, disaster relief and rescue, and family services [1]. In traditional industrial environments, the targets are known objects with fixed shapes and sizes, so the robot only needs to grasp them with a specific grasping configuration. However, in unstructured scenarios, the targets are often unknown objects with unfixed shapes and sizes and with different types. For these unknown objects, there still exist numerous challenges in generating the corresponding grasping configurations quickly and accurately [2]. In order to accurately describe a grasping pose, [PERSON] et al. [3] proposed the rectangular grasping representation method, which significantly contributed to subsequent research. [PERSON] et al. [4] developed two fully connected neural networks to identify the optimal grasping rectangle from a pool of potential grasp rectangle candidates. [PERSON] et al. [5] and [PERSON] et al. [6] subsequently integrated the convolutional neural network architecture into the grasp detection model. Although all the aforementioned methods initially produce a series of grasping rectangles and subsequently select the optimal one, none achieve very high grasp detection accuracy. To improve the grasp detection accuracy, [PERSON] et al. [7] introduced the method of target detection into grasp detection. However, its detection speed is excessively slow. To enhance the grasp detection speed, [PERSON] et al. [8] devised a lightweight fully convolutional neural network. This network directly predicts the grasp quality, grasp angle, and grasp width of each pixel, resulting in the final grasp configuration. [PERSON] et al. [9] and [PERSON] et al. [10] improved the network model on this basis, and both the detection speed and the detection accuracy are greatly improved. However, the aforementioned methods merely stack a few convolution layers during the feature extraction stage, failing to adequately extract features. Also, there is no use of multi-scalefeatures to enhance the grasping features. Additionally, the segmentation of graspable and non-graspable regions is inadequate, often resulting in incorrect grasping configurations. In order to solve the above problems and further improve the accuracy and speed of grasp detection, this article proposes the grasp detection network ODGNet. The network introduces omni-dimensional dynamic convolution (ODConv) [11] into the feature extraction stage to enhance the feature extraction capability of the grasping region; at the same time, the Shuffle Attention (SA) [12] module and the ODConv are used to form a grasping region feature enhancement and fusion (GREF) module, which enhances the feature learning ability of the grasping region, and suppresses the non-grasping region features; during feature reconstruction, the primary pixel features in the feature extraction part are fused with the advanced semantic features to further enhance the feature expression ability of the grasping region. The experimental results from the Cornell dataset [6] demonstrate that the algorithm presented in this paper outperforms in both grasp detection accuracy and detection speed. Moreover, the grasp detection performance with the multi-object dataset [13] surpasses that of the comparative method. Additionally, the simulated grasping experiments validate the efficacy of the proposed method.