# Research on Rapid Recognition of Moving Small Targets by Robotic Arms Based on Attention Mechanisms
## 2 Related Work  
### Introduction to the YOLOv8 Algorithm  
Target detection algorithms are categorized into one-stage and two-stage detection algorithms, with the primary distinction being the two-stage algorithm's clear separation of processes: initially extracting feature regions, and subsequently identifying these re gions [11]. Methods such as RCNN (region-CNN) [12] are used to extract multiple feature regions, followed by the deployment of classification algorithms such as SVM (support vector machine) or deep neural networks to determine if they match the target object. These tactics are usually time-consuming, rendering them less suitable for scenes requiring high real-time rates. One-stage target detection algorithms integrate the aforementioned processes, predicting the target region directly from the original image. Among these, the YOLO (You Only Look Once) algorithm is the most representative one-stage target detection model [13], offering major speed improvements over R-CNN type algorithms and thus providing feasibility for online real-time applications. Since 2016, this method has received considerable attention, leading to multiple improvements. Upgraded versions include YOLOv5 [14], YOLOv8 [15], and YOLOv9 [16]. The latest algorithm in the YOLO series has been updated to v9, but its fundamental framework remains rooted in the core principles of the YOLO series. YOLOv9 primarily focuses on the design of programmable gradient information (PGI) and generalized ELAN (GELAN), without altering the basic structure of YOLO. Therefore, after careful comparison, we have opted to continue using YOLOv8 as our foundational structure, given its relatively lightweight nature and proven effectiveness across various computer vision tasks and scenarios. YOLOv8 provides various scale models, including N/S/M/L/X, to accommodate different scene requirements. Table 1 shows the parameters of various scales of the YOLOv8 model. YOLOv8, as one of the most widely used versions in practical applications, not only inherits the features of previous versions, offering good recognition accuracy and fast recognition speed, but also enhances the performance of the model itself, enabling broader application in real production. Figure 1 presents the main structure of YOLOv8. The detection effect of YOLOv8 demonstrates significant improvement compared to other mainstream object detection models [17]. By improving the accuracy of the detection algorithm, YOLOv8 enables more accurate target identification. Early object detection was completed in simple environments; in the task of recognizing fast-moving small targets, improvements to the model are needed to accomplish identification tasks in more complex environments [18]. YOLOv8 replaces the C3 module in YOLOv5's backbone with the gradient-rich c2f module, reducing the number of blocks in the maximum stage of the backbone network. This further decreases the parameter amount and computational needs, realizing a more lightweight model [19]. YOLOv8 also eliminates the convolutional structure in the up-sampling stage of PAN-FPN from YOLOv5, thus speeding up computation. YOLOv8 \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**STRUCTURE** & **YOLOv8N** & **YOLOv8S** & **YOLOv8M** & **YOLOv8L** & **YOLOv8X** \\ \hline Depth & 0.33 & 0.33 & 0.67 & 1.00 & 1.00 \\ width & 0.5 & 0.50 & 0.75 & 1.00 & 1.25 \\ Max\_channels & 1024 & 1024 & 768 & 512 & 512 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: YOLOv8 model parameters at various scales. Figure 1: Structure of YOLOv8. employs an anchor-free detection approach, which predicts the center point and aspect ratio of the target directly instead of predicting the position and size of the anchor box. This method reduces the quantity of anchor boxes, leading to enhanced detection speed and accuracy [20]. This chapter gives a brief introduction to the new features of the new algorithm network. 1. We introduce and enhance a novel multi-scale parallel network structure, Faster-EMA-Net, which is based on the traditional EMA algorithm.