## 3 Experiment
### Algorithm
#### 3.1.4 Results and Analysis  
In this study, to comprehensively evaluate the performance of YOLOv5 in high-speed road detection tasks, we specifically designed a series of experiments comparing different versions of the YOLOv5 architecture. This was carried out to identify the most suitable version to serve as a baseline model for further optimization. We selected the YOLOv5n, YOLOv5s, YOLOv5m, YOLOv5l, and YOLOv5x models for comparison under uniform dataset and training strategy conditions. This approach allowed us to thoroughly assess the performance variations of each version in the specific application scenario of high-speed road detection. The results of these experiments are illustrated in Figure 9.  
As clearly demonstrated in Figure 9,b, YOLOv5n exhibits a notably weaker performance in terms of average precision and loss convergence compared to the other four models. Meanwhile, the performances of YOLOv5s, YOLOv5m, YOLOv5l, and YOLOv5x  
Figure 9: **(a) The comparison of mAP across different versions of YOLOv5. (b) The comparison of loss metrics across different versions of YOLOv5. **  
are closely matched. Due to its limited capacity, YOLOv5n struggles to fully learn data features, resulting in weaker generalization abilities. In contrast, larger models such as YOLOv5s, m, l, and x, with their increased number of parameters and deeper network architectures, are capable of learning more complex features. Furthermore, an in-depth analysis of the data in Table 3 reveals that the YOLOv5s model has significantly fewer parameters and requires fewer floating-point operations than YOLOv5m, YOLOv5l, and YOLOv5x. This reduced model complexity provides a solid foundation for edge computing applications. Considering these comparative insights, we have selected YOLOv5s as our baseline model. To validate the effectiveness of lightweight convolution using GSConv in the YOLOv5 network model, and to assess the impact of the optimal transport assignment (OTA) label allocation strategy on model accuracy, this study builds upon the YOLOv5s model. We introduced GSConv to replace the standard convolutions in the Neck section of the original YOLOv5s network, resulting in an improved algorithm termed YOLOv5s-L. Additionally, we incorporated the OTA label allocation strategy into the YOLOv5s-L framework, leading to a further enhanced model named YOLOv5s-Light-OTA. Both of these modified algorithms, YOLOv5s-L and YOLOv5s-L-OTA, were compared with the original YOLOv5s model in experiments conducted on the HSRD dataset. The results of these comparative analyses are detailed in Table 4.  
Observing the parameter count and the number of floating-point operations (FLOPs) for each model as presented in Table 4, it is evident that both YOLOv5s-L and YOLOv5s-L-OTA maintain consistency in these metrics. Compared to the original YOLOv5s network model, these modified versions exhibit a 21% reduction in parameters and an 18% decrease in FLOPs. The lightweight modifications to YOLOv5s-L and YOLOv5s-L-OTA result in an increase in frames per second (FPS), from 92 frames in the original model to 120 frames, achieving 1.3 times the original model's FPS. This enhancement signifies improved real-time responsiveness in practical computations. Regarding the mean average precision (mAP), our YOLOv5s-L algorithm achieved 94.7%, showing a slight decrease in detection accuracy by 0.8% compared to the original YOLOv5s. However, our YOLOv5s-L-OTA algorithm reached an mAP of 97.5%, which is a 2% improvement in detection accuracy over the original YOLOv5s. These data indicate that while the lightweight version slightly reduces accuracy, the incorporation of the OTA strategy significantly enhances it, demonstrating the effectiveness of our proposed modifications. To visually illustrate the variation in the mean average precision (mAP) values throughout the experimental results, we provide a comparative line graph depicting the changes in mAP values during training for both the proposed and the original models. As shown in