# 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  
To evaluate the effectiveness of the proposed method, we compared it with other mainstream object detection algorithms such as Faster-RCNN [34], YOLOv3 [35], and SSD [36], along with the latest YOLOv8n [37], and RT-DETR [38]. The experimental results are shown in Table 3. Faster R-CNN is a region proposal-based method, initially employing a Region Proposal Network (RPN) to extract potential target regions, followed by a classifier for categorization. It exhibits good accuracy but is characterized by a giant model size and slower speed. On the other hand, the SSD model forgoes region proposals and directly predicts the object classes and bounding boxes on feature maps of multiple scales, striking a balance between speed and accuracy. It operates faster than Faster R-CNN, albeit with a slightly lower accuracy than SSD. YOLOv3, a classic version in the YOLO series, performs predictions directly on the entire image, resembling SSD, but employs a different anchor box strategy and loss function. It boasts high accuracy, although it falls short in model size and speed compared to SSD. YOLOv8 is the latest model in the YOLO series, with YOLOv8n  
Figure 8: (**a,c,e**) represent the mouse hole detection results of YOLOv5n under complex background conditions; (**b,d,f**) represent the mouse hole detection results of CGT-YOLOv5n under complex background conditions. Figure 9: Comparison of detection results between the original and improved models using data captured at different heights by a drone: (**a,b**) at 2 m, (**c,d**) at 3 m, (**e,f**) at 4 m, (**g,h**) at 5 m, and (i,j) at 6 m.  
offering a variety of backbones and custom structures. While maintaining high accuracy, it achieves faster inference speeds than other object-detection models. However, YOLOv8n has a higher model complexity, necessitating more computational resources. RT-DETR is a real-time object detector based on the Vision Transformer (ViT), capable of efficiently handling multi-scale features by decoupling intra-scale interaction and cross-scale fusion. Nonetheless, its accuracy on mouse hole data is low and it has a larger model size. To fairly assess the overall performance of different models, this study employed the sum of ranking differences (SRD) method [39, 40]. As shown in Figure 10, this approach is based on a \(5\times 7\) matrix, with the rows representing various models and the columns representing evaluation metrics such as the mAP, FPS, model size, and inference speed. We calculate the absolute differences between the rankings of each solution and the reference rankings, and models with smaller SRD values are considered superior [41]. The results indicate that SSD, YOLOv5n, YOLOv8n, RT-DETR, and CGT-YOLOv5n performed the best, while Faster R-CNN exhibited the poorest overall performance. Due to the unique nature of mouse hole detection in grassland areas aimed at achieving mouse pest control, the following analysis will focus on the accuracy of mouse hole detection within the models exhibiting an optimal overall performance, specifically mAP_0.5 and mAP_0.5:0.95. Figure 10: Sum of ranking differences (SRD) graph, where Min represents the minimum reference standard; Rnk, Rnk1, Rnk2, Rnk3, Rnk4, Rnk5, Rnk6, and Rnk7 represent the rankings of the reference standard, Faster R-CNN, SSD, YOLOv3, YOLOv5n, YOLOv8n, RT-DETR, and CGT-YOLOv5n, respectively, from the least to the most significant. Diff1, Diff2, Diff3, Diff4, Diff5, Diff6, and Diff7, respectively, denote the differences between Faster R-CNN, SSD, YOLOv3, YOLOv5n, YOLOv8n, RT-DETR, and CGT-YOLOv5n and the reference standard.