## 1 Introduction  
With the continuous advancement of automation technology, vision-based robots have been increasingly applied in various domains such as industrial manufacturing [1], logistics and packaging [2], and intelligent agricultural harvesting [3]. Among these applications, object grasping and sorting are critical steps, as their efficiency and accuracy directly impact the overall productivity and quality of the workflow. Numerous studies have focused on enhancing the grasping capabilities of vision-based robots, yielding a range of significant advancements. For example, [PERSON] et al. proposed an operational relationship reasoning algorithm based on the VLMRN neural network model [4] and a real-time grasp region detection algorithm using the SE-RetinaGrasp network. They developed a grasping method specifically designed for stacked object scenarios by integrating the two approaches. However, the grasping success rate and target recognition accuracy of this method in complex stacking scenarios still need to be improved. [PERSON] et al. proposed an optimal grasping posture detection algorithm based on a dual-network architecture [5], which improves the detection speed and enhances the recognition performance for small target objects by modifying the YOLOv3 detection model. However, the algorithm relies on two deep learning networks, resulting in a more complex structure and higher computational costs, making it difficult to meet real-time application requirements. [PERSON] et al. proposed a grasping detection method tailored for various fruit stacking scenarios [6], which can determine the grasping parameters of the currently targeted object. However, it cannot perceive global scene information and grasp specific types of objects. Moreover, the relatively small size of the dataset limits its generalizability to broader applications. [PERSON] et al. combined the object detection algorithm (YOLOv5) with the fully convolutional grasp detection network (GDFCN) to propose a real-time grasp detection algorithm for robotic arms to handle unfamiliar objects [7]. This method effectively achieves object classification under real-time constraints and improves the stability and accuracy of grasp detection. Nevertheless, its performance under complex stacked scenes still has room for improvement. Another line of research focuses on end-to-end object pose estimation methods that learn to directly map input data to pose parameters, thereby eliminating the need for manually designed features and complex multi-stage processing. [PERSON] [8], [PERSON] [9], [PERSON] [10], and [PERSON] [11] investigated the use of convolutional neural networks (CNNs) to directly regress object poses from RGB images. By leveraging end-to-end training on large-scale datasets, these networks can automatically learn the mapping between image features and object pose. Such methods simplify the pose estimation pipeline and demonstrate strong performance under ideal conditions. However, end-to-end approaches still face several challenges, including a high dependency on the quality and diversity of training data, limited generalization ability in complex or cluttered environments, and suboptimal pose estimation accuracy. Additionally, the black-box nature of these models makes it difficult to interpret and optimize their internal decision-making processes. In summary, existing algorithms often struggle to meet real-time requirements while maintaining high detection accuracy. These limitations become even more pronounced in complex stacked scenes, where increased algorithmic complexity and restricted computational resources hinder performance. Furthermore, current methods frequently suffer from low grasp-success rates and inaccurate target recognition in such environments. To address the inability of current robotic grasping algorithms to balance detection accuracy and real-time performance, this paper proposes a novel approach that integrates deep learning-based object detection with 3D point cloud processing.