Deep Learning for Dynamic Wildlife Monitoring: A Real-Time Approach

With the rapid amount of deforestation around the world, wild animals lose their habitat and move closer to human settlements, leading to increased human-wildlife conflict and the loss of important human lives, livestock, and rare wild animal species. Wildlife monitoring is an essential task in computer vision for maintaining biodiversity, ecosystem balance and stability. It plays a vital role where traditional systems, such as camera traps and manual methods, fall short, and helps to avoid human-wildlife conflict. However, due to the challenging forest terrains, dynamic backgrounds, diverse animal poses, and low lighting conditions, existing methods struggle to efficiently extract features in real-time. These models suffer from limitations, including slow speed and low accuracy. To address these challenges, a novel real-time wildlife monitoring model has been introduced that leverages YOLOv11, designed to perform effectively in complex backgrounds, diverse animal poses and low-light conditions. A new wild animal dataset is developed, comprising 17 different wild animal species, captured under low-light conditions and in challenging backgrounds. The Grad-CAM technique is integrated with YOLOv11 to enhance the model's interpretability to generate heatmaps and attention maps. These maps help identify the important regions of the animal's body on which the model focuses more in real-time monitoring applications. The YOLOv11n model achieves an mAP of 97.4%, precision of 96%, and an F1-score of 95% on self-created wild animal dataset. The parameters of the fine-tuned YOLOv11 decreased by 30% relative to the previous version, resulting in a very small model size of 5.5 MB and reduced processing time. The model was evaluated on two other datasets: Animal2 and Camera Trap, with an mAP of 92% on the Animal2 dataset and 90% on the Camera Trap dataset.