## 4 Wildlife Testing Experiments and Analysis of Results
### Experimental Results & Analysis  
This research examines the target detection efficiency of the updated model on various types of animals before and after the improvement in order to analyze the usefulness of the improved algorithm on target detection for each animal. Table 2 presents the outcomes. The experimental results demonstrated that the detection effect of all types of animals was improved. The detection accuracy was high when the wild animals themselves could be easily distinguished from the surroundings, and it was lower when the target detection model's detection accuracy was more similar to that of the environment. The butterfly's mAP in the original YOLOv5s algorithm is the lowest, at just 87.7%, and the panda's mAP is the greatest, at 94.6%. The butterfly's mAP in the upgraded YOLOv5s algorithm is the lowest, at just 91.3%, and the tiger's mAP is the greatest, at 95.6%. The improved detection algorithm of YOLOv5s has improved the detection accuracy for large target animals, such as giraffes and elephants, due to the increased sensory field of the model, and the mAP for medium-sized animals, such as pandas and tigers can maintain the high detection accuracy before the improvement. There has been a major advancement in the identification of wild animals for some creatures, such as butterflies and squirrels, whose color patterns are protective or comparable to those of their natural habitat. This suggests that the enhanced YOLOv5s model presented in this study can more effectively carry out detection tasks for animals with a variety of sizes and morphologies in a variety of situations. For the improved YOLOv5s model, animals with different size targets and morphologies and animals with protective colors can have very good detection results, mainly attributed to the improvement of YOLOv5s network structure in expanding the sensory field. Four sample photographs were taken from the test set of the improved YOLOv5s model before and after for comparison. The results are given in Figure 12 to demonstrate the advantages of the enhanced YOLOv5s algorithm over the original YOLOv5s in this paper. The name of the identified target is listed in the detection box above it. The confidence level indicates how well the algorithm performed. The graphic shows that when compared to the original method, the improved YOLOv5s algorithm has a greater detection accuracy for photographs of animals. The improved model does not accidentally misdetect the tree branches, and the improved YOLOv5s has a higher confidence level for panda detection, as can be seen from the comparison plots in Figure 12a. The original YOLOv5s model mistakenly detects the tree on the left as a panda when detecting the panda image because the tree branches overlap with the panda. The comparison graph in Figure 12b shows that the confidence level of the improved YOLOv5s model is significantly higher than the original YOLOv5s model for detecting giraffes when the three giraffes have overlapping bodies. The updated YOLOv5s model now has better feature extraction and feature fusion capabilities for wildlife photographs. It has good detection capacity in various natural environments, according to the examination of the above sample image detection results. In terms of detection and recognition, the enhanced YOLOv5s model performs better than the baseline model and dramatically lowers the rate of missed detection and erroneous detection of target animals. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Butterfly** & **Panda** & **Squirrel** & **Tiger** & **Elephant** & **Giraffe** \\ \hline YOLOv5s & 87.7 & 94.6 & 89.3 & 94.2 & 90.0 & 90.0 \\ YOLOv5s\_ours & 91.3 & 94.1 & 95.5 & 95.6 & 94.2 & 94.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experimental results of various types of wildlife testing before and after improvement. Experiments on animal detection and ablation were carried out using both the initial YOLOv5s model and the enhanced YOLOv5s model.