## 4 Experiment
### Ablation Experiments  
To verify the effectiveness of each improved module and their respective effects on the model, the first group of experiments was conducted after adding different improved modules under the same test set. The second group of ablation experiments was performed on the basis of the benchmark model, incorporating the Ghost module. The version of the baseline model was YOLOv5s, and the results of the experiment are shown in Table 7 (The best value in the same indicator is bolded). The visualizations of the results from the ablation experiment are shown in Figures 15-19. To help the reader understand the results of the ablation experiments more distinctly, the visualized results of the two groups of experiments are given in the following figures (Figures 15-19), which were plotted based on the different performance metrics. In the first group of experiments, four improved modules were sequentially added to the benchmark model, resulting in Models 2 to 5. Specifically, the mAP\({}_{0.5}\) and mAP\({}_{0.95}\) of the benchmark model (YOLOv5s) are 78.2% and 47.7% respectively, while its GFLOPs and FPS are 16.6 and 51.8 respectively. From the experimental results in Table 7, all four modules used in this paper are effective, which indicates the validity of these four modules in this model. The detailed discussion is listed as follows. Figure 16: The GFLOPs of each model in the ablation experiment. Figure 15: The params of each model in the ablation experiment. Firstly, the improvement of the loss function SIoU increases the model's consideration of the vector angles required for the expected regression, improving the model's inference and accuracy. Compared to the benchmark Model 1, Model 2 improves mAP\({}_{0.5}\) by 2.1% and FPS by 1.1, respectively. Secondly, the SECSP attention module increases the sensitivity of the model to the channel features and effectively improves the feature extraction capability of the model, which increases the mAP\({}_{0.5}\) and the FPS of Model 3 by 1.2% and 3.4, respectively. Figure 17: The mAP\({}_{0.5}\) of each model in the ablation experiment. Figure 18: The mAP\({}_{0.95}\) of each model in the ablation experiment. Thirdly, the GSCneck is integrated into Model 4 to reduce the computational complexity, which accomplishes the task of improving the model inference speed and detection accuracy. Then, the mAP\({}_{0.5}\) is increased by 4.4% and the FPS is improved to 58.1. Eventually, although the Ghost module is not as good as the GSCneck module in terms of improving accuracy and speed, the addition of Ghost to the Model 5 simplifies the parameters and computational costs dramatically when compared to the previously discussed models. This achieves the lightweight construction of the model. With the addition of Ghost's reconstructed backbone, the model GFLOPs are reduced by 9.4 and the parameters by 2.81 M.  
In the second group of experiments, all the models based on Ghost modules (Models 6 to 10) show better results compared to Model 1, which indicates that the combination of these modules with Ghost is effective. Moreover, the computational costs and parameters of all Ghost-based models are substantially decreased when compared to the first group of experiments. The detailed comparison results between the benchmark Model 5 and other models such as Models 6-10 are listed as follows. Firstly, the combination of the two modules SECSP and GSCneck with Ghost (Models 6 and 8) respectively, both achieve further growth in model speed and accuracy. However, the model with the addition of SECSP requires additional processing time for the extraction of the channel features, which causes a slight decrease in the speed of the model within the combination of SECSP and Ghost (Model 7). The results show that the FPS of Model 7 is reduced by 0.3, but the mAP\({}_{0.5}\) and mAP\({}_{0.95}\) of Model 7 are increased by 1.3% and 2.2%, respectively.