# CGT-YOLOv5n: A Precision Model for Detecting Mouse Holes Amid Complex Grassland Terrains
## 3 Experimental Results and Discussion
### Comparison and Discussion of Experimental Results from Various Models
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Models** & **mAP (\%)** & **mAP (\%)** & **Model Size** & **Latency** & **FPS** \\  & **IoU = 0.5** & **IoU = 0.50.95** & **(M)** & **(ms)** & **(f/s)** \\ \hline Faster R-CNN & 84.1 & 36.0 & 315.0 & 64.9 & 15.4 \\ SSD & 83.3 & 33.7 & 100.2 & 8 & 125 \\ YOLOv3 & 89.2 & 45.2 & 117.8 & 11.3 & 80.6 \\ YOLOv5n & 89.5 & 42 & 3.7 & 2.4 & 217.4 \\ YOLOv8n & 90.1 & 42.5 & 6 & 2.4 & 277.7 \\ RT-DETR & 83.0 & 33.8 & 63.1 & 8.0 & 119.0 \\ CGT-YOLOv5n & 92.8 & 46.3 & 15.4 & 4.3 & 161.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Experimental results for different models. Among the five models with superior performance, the RT-DETR model has the lowest mAP_0.5 of 83%. The SSD model has the lowest mAP_0.5:0.95 of 33.7%. Moreover, the two models only have a slight difference of 0.3% and 0.1% in mAP_0.5 and mAP_0.5:0.95, respectively, and their larger model size is unfavorable for deployment on uncrewed aerial vehicles (UAVs). On the other hand, the other three models, with their compact size and faster detection speed, are more suitable for UAV deployment. Among the remaining three models, CGT-YOLOv5n has the highest mAP_0.5 of 92.8%, while YOLOv5n has the lowest mAP_0.5 of 89.5%. YOLOv8n, with a mAP_0.5 of 90.1%, sits in between. The mAP_0.5 of CGT-YOLOv5n is 3.3% and 2.7% higher than those of YOLOv5n and YOLOv8n, respectively. Also, CGT-YOLOv5n has the highest mAP_0.5:0.95, which is 4.3% and 3.8% higher than YOLOv5n and YOLOv8n, respectively. Regarding model size and detection speed, CGT-YOLOv5n slightly lags behind YOLOv5n and YOLOv8n. However, considering the characteristics of mouse hole detection in grasslands, where high accuracy is crucial and detection time is not overly stringent, a minor sacrifice in detection time is within an acceptable range. Therefore, with its superior overall performance and the highest detection accuracy, CGT-YOLOv5n is more apt for mouse hole detection in complex grassland terrains. Figure 11 displays the detection results of the seven models mentioned in the text on complex grassland backgrounds. The presence of large areas of weeds, severe shadows on the grassland, and traces of rainwater erosion significantly affect the models' detection of mouse holes. Figure 10(a) depicts the detection result by the Faster R-CNN model, where it misidentifies the shadow of weeds as mouse holes in five instances. Figure 10(b) portrays the detection results for SSD, which misses two mouse holes and incorrectly identifies a shadow of grass as a mouse hole in one instance. Figure 10(c) presents the detection results for YOLOv3, which misidentifies the shadow of grass as mouse holes in four instances. Figure 10(d) shows the detection results for YOLOv5n, with one missed mouse hole and two instances of misidentifying the shadow of grass as mouse holes. Figure 10(e) displays the detection results for YOLOv8n, which was relatively good yet still misidentified the shadow of grass as a mouse hole in one instance. Figure 10(f) presents the detection results for the RT-DETR model, detecting all three mouse holes but misidentifying shadows as mouse holes in five instances. Figure 10(g) illustrates the detection results for the improved model, detecting all three mouse holes without misidentifying shadows as mouse holes. Based on the results, it is evident that missed detection and shadow interference are challenging issues in mouse hole detection. The models Faster R-CNN, SSD, YOLOv3, YOLOv5n, YOLOv8n, and RT-DETR tend to misidentify the shadows of interfering objects  
Figure 11: Detection results for mouse holes on complex backgrounds using different models: (**a**) Faster R-CNN; (**b**) SSD; (**c**) YOLOv3; (**d**) YOLOv5n; (**e**) YOLOv8n; (**f**) RT-DETR; and (**g**) CGT-YOLOv5n. as mouse holes. In contrast, the improved model CGT-YOLOv5n, which integrates CAM, TSCODE, and ODconv, can differentiate between shadows and mouse holes, enabling a better detection performance in drones on complex grassland backgrounds.