## 2 Methodology
### YOLOv8-Uire  
#### 2.1.1 Overview  
YOLOv8 is a relatively novel one-stage target detection algorithm in the YOLO series, and has faster speed, accuracy, and performance compared with other mainstream target detection algorithms. As a state-of-the-art (SOTA) model, YOLOv8 is released in five versions--YOLOv8n, YOLOv8s, YOLOv8m, YOLOv8l, and YOLOv8x--with the number of parameters and floating-point operations (FLOPs) increasing progressively across the versions. Considering the requirements of real-time performance, computational efficiency, and detection accuracy in industrial environments, we selected YOLOv8n as the baseline model for our application. As illustrated in Figure 2, the YOLOv8-URE algorithm, mainly comprises two key components: the backbone module (Figure 2a), responsible for extracting feature information, and the neck module (Figure 2b), which further enhances the feature maps by introducing multi-scale feature fusion, thereby improving the model's capability to perceive and localize targets accurately.  
Figure 1: Overall system framework of object detection and grasping.  
In the backbone module, to enhance the feature extraction capability for stacked objects, the feature extraction network incorporates C2f_UniRepLKNetBlock. This structure employs a small number of large convolutional kernels to ensure a broad and effective receptive field while leveraging small kernels to efficiently capture more complex spatial patterns. Additionally, multiple lightweight blocks are used to increase network depth, thereby further enhancing its representational capacity. Efficient local attention (ELA) [12] is introduced as an attention mechanism that addresses the limitations of traditional coordinate attention methods. Specifically, it highlights the poor generalization capability caused by batch normalization, the adverse effects of dimensionality reduction on channel attention, and the high computational complexity of conventional attention generation. By combining 1D convolution with group normalization-based feature enhancement, ELA efficiently encodes two 1D positional feature maps without dimensionality reduction. This enables precise localization of regions of interest while maintaining a lightweight architecture.  
In the neck module, to enhance the feature fusion capability for stacked objects, this paper redesigns the neck by integrating CSPStage and DySample mechanisms into a newly designed CSPStage-Dy module. CSPStage employs different channel numbers for features at varying scales, allowing flexible control over the representation capacity of both high-level and low-level features under lightweight computational constraints. Additionally, the redundant upsampling operations in the original queen-fusion structure are removed, which significantly reduces inference latency while maintaining only a minimal drop in  
Figure 2: Structure of the YOLOv8-URE network: (**a**) backbone module; (**b**) neck module; (**c**) head module; (**d**) SPPF module; (**e**) detection layer.  
accuracy. Furthermore, the original convolution-based fusion method is optimized using CSPNet connections, and the concepts of re-parameterization and the ELAN structure are introduced to further enhance accuracy without significantly increasing the computational load. Moreover, DySample learns pixel-wise weights, enabling the network to adopt adaptive sampling strategies across different regions, especially around object boundaries, thus improving edge clarity and detection precision.  
Finally, Shape-IoU [13] is used to replace the original CIoU loss function to accelerate model convergence and improve regression speed.