This study explores the application of object detection models for detecting Arctic Foxes in camera trap images, a crucial step towards automating wildlife monitoring and enhancing conservation efforts. The study involved training models on You Only Look Once version 7(YOLOv7) architecture across different locations using k-fold cross-validation technique and evaluating their performance in terms of mean Average Precision (mAP), precision, and recall. The models were tested on both validation and unseen data to assess their accuracy and generalizability.
The findings revealed that while certain models performed well on validation data, their effectiveness varied when applied to unseen data, with significant differences in performance across the datasets. While one of the datasets demonstrated the highest precision (88%), and recall (94%) on validation data, another one showed superior generalizability on unseen data (precision 76%, recall 95%).
The models developed in this study can aid in the efficient identification of Arctic Foxes in diverse locations. However, the study also identifies limitations related to dataset diversity and environmental variability, suggesting the need for future research to focus on training models during different seasons and having different aged Arctic Foxes. Recommendations include expanding dataset diversity, exploring advanced object detection architectures to go one step further and detect Arctic Foxes with skin diseases, and testing the models in varied field conditions.