<p>Limited memory and resources frequently hinder real-time detection and pose solving for space robot grasping in unmanned deep space exploration equipment. To achieve a high accuracy, high speed, and robust robotic grasping posture detection system under the condition of low parameters, a lightweight network for robotic visual grasp based on pixel-level detection is proposed. (1) To simplify the spatial complexity of the network and improve detection accuracy, a GS-net was employed. Grouped dot convolution reduces the number of parameters in the convolutional layer, and instance normalization performs independent segmentation for each convolutional layer of feature map on a per-image and per-channel basis, which speeds up the convergence of network and enhances detection precision. Subsequently, the SE module assigns weights to each feature channel of the feature maps outputted by the grouped dot convolution, in order to extract feature information with high weights. (2) To address the issue of edge information loss during upsampling and enhance the detection accuracy, a method involving bilinear interpolation of features between downsampling and upsampling is employed. By performing bilinear interpolation on feature maps of the same dimension between downsampling and upsampling, the resolution of the output feature maps is increased, which, in turn, further improves the detection accuracy. On the selected Cornell data test set, the network trained in this paper achieves a detection accuracy of 96.6%, with a detection speed of 25.3&#xa0;ms, and the network model parameters are 0.365 million. Additionally, to extend robotic grasp detection into the space, we created our own satellite model and designed corresponding experiments to verify detection performance under different environmental conditions. Ultimately, PyBullet simulation experiments on the Dex-Net 2.0 dataset demonstrated that the research can be effectively applied to practical applications.</p>