## 1 Introduction  
With the widespread implementation of robots in various fields, grasping technology, which plays a crucial role in controlling robots, has gained significant prominence. Currently, the majority of grasp detection methods still heavily rely on established object models and contextual information, rendering it inadequate for tackling the challenging task of recognizing complex unknown objects. Deep learning-based methods [1] have made significant progress, along with the continuous development of deep learning technology, in the field of grasp detection in recent years. [PERSON] et al. [2] first proposed a grasp representation based on 2D-oriented grasp boxes, optimizing the expression of grasp poses and enabling direct grasp detection through object detection. Since then, researchers have focused on improving the accuracy and efficiency of grasp detection. [PERSON] et al. [3] employed a two-stage cascaded network for grasp pose detection, achieving an accuracy of 73.9% on the Cornell grasp dataset. Due to the extensive number of network parameters, real-time performance was compromised, and generalization capabilities were limited. [PERSON] et al. [4] trained an end-to-end network using deeper networks with input RGD (red, green, depth) data to obtain grasp detection results directly, achieving 88.0% accuracy on the Cornell dataset. [PERSON] et al. [5] proposed a generative grasp convolutional neural network that utilizes depth images as input and directly outputs the precise grasp location, angle, and jaw width, thereby enabling real-time object grasping by robotic arms. [PERSON] et al. [6] proposed GRCNN, a convolutional neural network capable of processing multi-modal inputs such as RGB, RGD, and RGB-D data. By employing regression techniquesfor grasp pose prediction, the model achieved an impressive accuracy rate of 96.6% on the Cornell grasp dataset.  
To enhance real-time performance and accuracy in the grasp detection process, this paper proposes LGAR-Net2, a grasp network based on a loss-guided collaborative attention mechanism. RGB input is utilized to generate grasp detection results with identical dimensions as the input image, achieving pixel-level detection precision. In the coding stage, multi-layer step convolution is employed for deep semantic extraction preparation. The semantic extraction stage incorporates a convolution structure that combines residual architecture and a loss-guided collaborative attention mechanism to enhance network robustness in detecting irregular objects. In the decoding stage, a bilinear interpolation layer replaces the inverse transpose convolution layer to ensure smoother pixel representation. The contributions of this paper are summarized as follows:  
* We propose a novel grasp detection network that integrates a loss-guided collaborative attention mechanism with multi-scale feature fusion to enhance the network's focus on object grasp regions.
* In order to address the checkerboard artifacts in the upper sampling layer and improve the grasping accuracy, an effective convolutional decoding block is proposed.
* The grasping network LGAR-Net2 built with the proposed module achieved 97.7% performance in both open datasets and simulated robot grasping. And an ROS simulation environment is established for the real-time prediction of grasping poses using LGAR-Net2, achieving a processing speed of 15 ms per piece with an accuracy rate of 93.3%.