## 4 Result Analysis of Detection Model
### Testing Phase
The performance assessment of a classification model on several animal species is summarized in Table 3. The measurements provide valuable insights into the model's ability to distinguish between various species effectively. The model has a notable level of sensitivity, as evidenced by the high sensitivity for bats (98.75%) and porcupines (98.31%). These results indicate the model's proficiency in accurately identifying positive cases. The model demonstrates the ability to accurately detect negative instances, as indicated by the specificity metric. Specifically, the wild boar class exhibits a specificity value of 95.92%, while the elephant class exhibits a specificity value of 90.91%. The precision of the model remains consistently high for all categories, particularly for goat (97.96%) and squirrel (98.86%), indicating its ability to generate accurate positive predictions. This table also demonstrates the model's inclination towards developing adverse predictions, as indicated by the high unfavorable predictive values observed for most species. The research exhibits a commendable achievement in achieving low rates of false positives, revealing just a limited number of cases where predictions were incorrectly positive. The results of this study illustrate the model's high level of precision and reliability when it comes to detecting as well as classifying various animal species. Table 4 contains the key performance indicators for a detection model within a research endeavor. These metrics comprehensively evaluate the model's ability to distinguish between diverse groups. The model's sensitivity of 96.65% demonstrates how well it can identify pleasurable events. With a specificity rating of 99.57%, the model can identify unfavorable circumstances. The model's ability to make accurate optimistic predictions is illustrated by the model's accuracy rate of 97.07%. The model's high negative predictive value of 99.57% demonstrates how accurately it predicts adverse outcomes. False positive occurrences are exceedingly uncommon, as indicated by their low frequency (0.42%). With a mistaken discovery rate of 2.93%, false positive predictions are distinctive. The false negative rate of 3.34% represents instances in which true positives were neglected. Due to its low value, the F1 score of 9.68% should be interpreted cautiously; further investigation may be required. The 96.41% Matthews Correlation Coefficient demonstrates the overall correlation between the model's predictions and the actual outcomes. These metrics support the comprehensive evaluation of the model's performance in the research environment by providing an in-depth analysis of the model's benefits and drawbacks. The model achieves a remarkable average precision of 94% for the \"Indian Crested Porcupine\" class on both the validation and test sets. This high score demonstrates the model's ability to predict this class's instances accurately. The model's performance remains strong across different classes, with average precision scores ranging from 86% to 94%. Particularly noteworthy is the fact that both the \"Indian Crested Porcupine\" and \"Wild Boar\" classes achieve the highest precision score of 94%, showcasing the model's proficiency in identifying these classes correctly. The model exhibits outstanding capabilities in this  
Figure 11: Recall–confidence and precision–confidence curve. detection task by analyzing the overall performance. With an average precision of 90% on the validation set and 89% on the test set for all classes combined, the model demonstrates high accuracy in classifying instances accurately. Furthermore, the comparable performance of the model on the test set to that on the validation set indicates its ability to generalize well to unseen data. This aspect highlights the model's robustness and demonstrates that it has been well trained. Therefore, it is evident that our model has proven to be robust, efficient and valuable for detecting endangered animals and giving extra protection to farms.