## 3 Introduction to the Dataset
### Analysis of Experimental Results  
By monitoring the loss and accuracy curves during training, we further validated the effectiveness of the hyperparameter settings in preventing overfitting. To better understand the computational complexity and performance trade-offs of different YOLOv8 versions tested in this study, we summarize the key metrics (parameters, gradients, GFLOPs) for each model version in Table 3. This comparison highlights the impact of additional modules on the model size and computational demand. In Table 3, the standard lightweight version of YOLOv8n has fewer parameters, making it suitable for real-time applications. By adding the CoT module to YOLOv8n, the model's ability to capture contextual information is enhanced, although this slightly increases the parameters and computational load. Additionally, using the DRConv module in two convolutional layers significantly improves the model's performance while maintaining reasonable computational complexity. As observed from Table 3, adding the CoT and DRConv modules increases the \begin{table}
\begin{tabular}{c c c} \hline \hline
**Data** & **Train** & **Val** \\ \hline Total images & 5840 & 648 \\ All labels & 41,211 & 4764 \\ Mousehole0 & 16,533 & 1852 \\ Mousehole1 & 15,282 & 1804 \\ Stone & 7479 & 902 \\ Cow dung & 1917 & 206 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Distribution of the number of upland sage-grouse hole datasets. \begin{table}
\begin{tabular}{l l} \hline \hline
**Hyperparameter** & **Value** \\ \hline Initial learning rate (lr0) & 0.01 \\ Final OneCycleLR learning rate (lrf) & 0.01 \\ Momentum & 0.937 \\ Weight decay & 0.0005 \\ Warmup epochs & 3.0 \\ Image augmentations & HSV hue, saturation, and value enhancements \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Hyperparameter settings. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Model Name** & **Parameters** & **Gradients** & **GFLOPs (FLOPs per Second)** \\ \hline YOLOv8n & 2,975,439 & 2,975,423 & 11.9 \\ COT & 3,017,711 & 3,017,695 & 12.0 \\ DRConv & 2,968,531 & 2,968,515 & 46.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Model comparison for YOLOv8 variants. computational complexity in terms of parameters and GFLOPs. Specifically, the DRConv module significantly raises the GFLOPs (from 11.9 to 46.1), reflecting the added computational cost. Despite this, both modules contribute to improved performance metrics, as shown in the results of Table 4 and Figures 6-8. This trade-off between computational cost and performance improvement is critical in real-world applications where model efficiency is as important as accuracy. These findings highlight the importance of balancing the computational complexity with the performance gains. While the additional computational cost might seem significant, the improvements in model accuracy and feature recognition capabilities justify the trade-off, especially in the context of complex tasks such as rodent detection and environmental monitoring. The ability to enhance performance without severely compromising efficiency is essential for deploying models in large-scale real-time applications. This figure illustrates the various losses on the training set, including the box loss, segmentation loss, classification loss, and DFL loss. These curves show that the model effectively learned the data features during training, with all losses significantly decreasing. This figure presents the variations in various losses on the validation set, including the box loss, segmentation loss, classification loss, and DFL loss. As training progresses, the losses gradually decrease, demonstrating good convergence of the model. This figure shows the changes in precision, recall, and mean average precision (mAP50 and mAP50-95) over time during training. As the training progresses, these metrics steadily improve, indicating that the model's detection capability continues to be enhanced.