# Grasp Detection Combining Self-Attention with CNN in Complex Scenes
## 2 Related Work  
To enable robots to determine optimal grasp angles and opening distances, it is necessary to have accurate modeling of the position, posture, and contour information of objects for precise grasp detection. Due to the constantly changing and complex characteristics of robot work environments, extracting and mapping relevant features is critical for effective object-background discrimination. Earlier methods for grasp detection primarily relied on non-data-driven traditional algorithms, including analytical approaches. Such approaches analyze the surface properties of objects related to friction at contact points and apply geometry, kinematics, and dynamics to calculate corresponding grasps [2]. Despite their potential advantages, such approaches can be challenging to apply in real-world settings primarily due to their requirement for manually engineered features. Recently, learning-based approaches have gained widespread attention and become the primary focus of research in grasp detection [4; 5; 8; 9]. Supervised learning is employed  
Figure 1: Examples of detection by grasping CNN [7]. to fit detection models in the dataset, allowing direct prediction of the grasp from the image [8; 9], without the need to construct a three-dimensional model of the object. A new five-dimensional grasp rectangle representation is proposed in [4] as an alternative to the grasp point prediction model. This representation includes the grasp center point, the opening distance of the end effector, and the rotation angle, directly replacing the grasp representation based on three-dimensional space. The optimum grasp region is determined using a cascaded two-stage support vector machines (SVM) classifier. The deep learning method based on neural networks has achieved great advantages in image classification [10], and [PERSON] [5] introduced deep neural networks (DNNs) as the grasp detection classifier, designing a two-stage DNN to avoid manual feature design and improving the generalization ability of the model. Each potential grasp rectangle is evaluated and ranked. Based on the oriented rectangle, grasp detection is similar to object detection in computer vision, so many classic CNN structures [10; 11; 12] are applied in grasp detection research to improve the algorithm performance. In [6], [PERSON] proposed a one-stage grasp detection network based on AlexNet, treating the calculation of the grasp rectangle as a regression problem, and achieved feature extraction and grasp rectangle prediction evaluation solely based on object image information. Furthermore, [PERSON] [13] utilized ResNet-50 [11] to extract features on the image and used linear SVM as the prediction classifier to predict the object's grasp configuration from the features extracted from the last hidden layer of ResNet-50. Similarly, using CNN structures, ref. [7] encodes the input image's features with downsampling convolution layers, increases network depth and abstract generalization ability with ResNet modules, and decodes pixel-level grasp prediction with upsampling layers. To summarize, existing research on grasp detection using deep learning technology primarily relies on commonly used CNN models for object detection like AlexNet, VGGNet, and ResNet. However, grasp detection and object detection differ significantly in their application scenarios. The former has more diverse application scenarios, and therefore, the grasp configuration of objects is complex and variable, requiring stricter parameters for grasp angle and position. The VIT [14] replaces the traditional CNN model with the transformer [15] to extract image features. It proposes an end-to-end detection architecture that exhibits excellent performance in image classification tasks. The transformer has become a new paradigm in computer vision due to its exceptional ability to model long sequences and extract global features. [PERSON] [16] demonstrates the feasibility of using the transformer for grasp detection tasks.