# A YOLOW Algorithm of Water-Crossing Object Detection
## 4 Experimental Results and Analysis
### Experiment Details and Settings
#### 4.3.2 Parameter Settings of YOLOv5  
In YOLOv5, the stochastic gradient descent (SGD) optimizer and the early stop strategy are employed, of which the learning rate and momentum are set to 0.01 and 0.937, respectively. In the training phase of YOLOv5, 60% of the raw images are captured as the training set and 40% as the validation set. The epoch and batch size of the network are set to 300 and 20, respectively. We conducted various experiments to analyze the effectiveness of the proposed and introduced modules in YOLOW. After conducting experiments on the Flow dataset, we obtained the model configurations and evaluation metrics shown in Table 1. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Model** & **\#Param.** & **Size** & **C2f** & **SPD** & **SPDCS** & **SPPAUG** & **P** & **R** & **mAP\({}_{\mathbf{0.5}}^{\textit{ed}}\)** & **mAP\({}_{\mathbf{0.75}}^{\textit{ed}}\)** & **mAP\({}_{\mathbf{0.5},\mathbf{0.95}}^{\textit{enf}}\)** & **FPS** \\ \hline
1 & 7.2M & 640 & \(\times\) & \(\times\) & \(\times\) & \(\times\) & 81.8\% & 73.4\% & 78.7\% & 27.2\% & 36.0\% & 106.4 \\
2 & 8.5M & 640 & \(\checkmark\) & \(\times\) & \(\times\) & \(\times\) & 83.5\% & 74.0\% & 79.1\% & 28.9\% & 36.8\% & 126.6 \\
3 & 8.8M & 640 & \(\times\) & \(\checkmark\) & \(\times\) & \(\times\) & 84.9\% & 74.3\% & 80.4\% & 29.2\% & 37.3\% & 101.6 \\
4 & 13.2M & 640 & \(\times\) & \(\times\) & \(\checkmark\) & \(\times\) & 85.7\% & 74.1\% & 81.6\% & 31.0\% & 38.1\% & 82.7 \\
5 & 13.7M & 640 & \(\times\) & \(\times\) & \(\times\) & \(\checkmark\) & 84.5\% & 72.9\% & 79.3\% & 27.4\% & 36.6\% & 105.3 \\
6 & 14.9M & 640 & \(\checkmark\) & \(\times\) & \(\times\) & \(\checkmark\) & 85.6\% & 72.6\% & 79.6\% & 29.4\% & 37.0\% & 111.1 \\
7 & 11.0M & 640 & \(\checkmark\) & \(\checkmark\) & \(\times\) & \(\times\) & 82.9\% & 75.4\% & 81.0\% & 29.2\% & 37.7\% & 120.5 \\
8 & 14.4M & 640 & \(\checkmark\) & \(\times\) & \(\checkmark\) & \(\times\) & 84.4\% & 76.6\% & 81.9\% & 30.5\% & 38.7\% & 90.6 \\
9 & 15.2M & 640 & \(\times\) & \(\checkmark\) & \(\times\) & \(\checkmark\) & 82.9\% & 74.7\% & 81.1\% & 30.3\% & 38.2\% & 106.4 \\
10 & 19.6M & 640 & \(\times\) & \(\times\) & \(\checkmark\) & \(\checkmark\) & 83.8\% & 75.6\% & 81.8\% & 32.0\% & 38.8\% & 85.6 \\
11 & 16.4M & 640 & \(\checkmark\) & \(\checkmark\) & \(\times\) & \(\checkmark\) & 85.5\% & 73.9\% & 81.4\% & 30.6\% & 38.1\% & 107.6 \\ YOLOW & 20.9M & 640 & \(\checkmark\) & \(\times\) & \(\checkmark\) & \(\checkmark\) & 87.0\% & 75.1\% & 82.1\% & 30.7\% & 38.3\% & 93.2 \\ Improve & +13.7M & 640 & \(\checkmark\) & \(\times\) & \(\checkmark\) & \(\checkmark\) & +0.52 & +0.17 & +0.34 & +0.35 & +0.23 & \(-13.2\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of Object Detectors on Flow Dataset. Table 1 shows that the simplest Model-1 without the C2f, SPD, SPPFAUG, and SPDCS modules have the lowest performance. Model-2, adding the C2f module, achieves better performance compared with Model-1, demonstrating the effectiveness of the C2f module. Similarly, Models 3 to 5, solely utilizing the SPD, SPDCS, and SPPAUG modules, achieve better performance compared with Model-1, respectively, which demonstrates the effectiveness of these modules. Furthermore, Model-5, adding the SPDCS module, achieves better performance than Model-4, demonstrating the superiority of the SPDCS module. Models 6 to 9 were trained by combining two modules to further demonstrate the improved performance obtained by combining modules compared with using a single module. Finally, our proposed method using a combination of the C2f, SPD, and SPPAUG modules outperformed the other models using different combinations of these three modules (Model-10). Although the YOLOW algorithm proposed in this paper has some shortcomings compared with the basic YOLOv5s algorithm in terms of detection speed, it can still meet the requirements of real-time detection and has a significant improvement in accuracy compared with YOLOv5.