## 3 Eye-Machine Interaction Technology Based on the YOLOv5 Network
### Experimental Design of Eye-Machine Interaction Technology
#### 3.1.3 Analysis of Experimental Results of Improved Model Structure  
For the improvement of the Anchor and the introduction of attention mechanism optimization, the improved YOLOV5 model is shown in Figure 9. While the introduction of a small target layer and the CA (Coordinate Attention) mechanism significantly improve the model's accuracy in detecting small objects and enhancing feature representation, these enhancements come with additional computational costs. The small target layer increases computational demand, especially when processing higher resolution images, as it adds more detection layers to better handle smaller objects. Similarly, the CA mechanism requires more computational resources to capture global contextual features, increasing both the inference time and memory usage. Although these improvements boost accuracy, they may introduce challenges in resource-constrained environments, such as embedded devices or real-time applications. It is essential to balance these extra computational costs against the gains in detection precision, depending on the specific application needs. Figure 9: Improved YOLOv5 model structure. In Figure 9, (a) shows the overall improved model structure, where the part circled by the blue dashed line is the introduced CA attention mechanism. The backbone network includes Focus (Focus) and C3 modules. The structure of the Focus module is shown in (b). Operations such as slice, Concat, and the convolution (CBL) of the original feature map could reduce the calculation amount, improve the speed, and realize subsampling. The structure of SPPF module is shown in (c). After three MaxPool operations, multi-dimensional pooling and feature fusion of input features were carried out to improve the expression ability of feature maps. The composition of CBL, as shown in (d), consists of a series of convolution, batch normalization, and activation functions for better understanding and extracting features; the Bottleneck module, as shown in (e), is composed of a series of convolution layers, which were used to reduce the number of channels and spatial dimensions of feature graphs, reduce the number of parameters, and retain important feature information. The C3 module, as shown in (f), divides the input into two convolutions (CBLs) for output to enhance the model's ability to learn features at different levels. During the training of the YOLOv5 model and the improved YOLOv5 model, the same dataset and the same parameter settings were used to draw the training loss comparison curve of the two models as shown in Figure 7 according to the log files saved during the training process. Figure 10 shows the performance of the improved YOLOv5 model in terms of target classification, location regression, and confidence loss. These loss indicators reflect, respectively, the model's ability to identify the category of the target in the image, determine the accuracy of the target location, and assess the probability of the presence of the target in the prediction box. With the progress of model training, we observed that all loss indexes showed a downward trend, indicating that the performance of the model was improving in all aspects. In addition, the steady decline in the loss function also suggests that the model was gradually converging, meaning that the network was learning and absorbing information from the training data. By comparing the loss curve between the improved model and the  
Figure 11 provides a detailed view of the average precision (AP) of the improved YOLOv5 model in object detection tasks, which is a key metric for measuring overall model performance. The figure reveals the model's performance in recognizing different classes of objects, where each class's AP value reflects the balance between accuracy and recall of the model's predictions.