WilDect-YOLO: An efficient and robust computer vision-based accurate object localization model for automated endangered wildlife detection

Objective. With climatic instability, various ecological disturbances, and human actions threaten the existence of various endangered wildlife species. Therefore, an up-to-date accurate and detailed detection process plays an important role in protecting biodiversity losses, conservation, and ecosystem management. Current state-of-the-art wildlife detection models, however, often lack superior feature extraction capability in complex environ-ments, limiting the development of accurate and reliable detection models. Method. To this end, we present WilDect-YOLO, a deep learning (DL)-based automated high-performance detection model for real-time endan-gered wildlife detection. In the model, we introduce a residual block in the CSPDarknet53 backbone for strong and discriminating deep spatial features extraction and integrate DenseNet blocks to improve in preserving critical feature information. To enhance receptive field representation, preserve fine-grain localized information, and improve feature fusion, a Spatial Pyramid Pooling (SPP) and modified Path Aggregation Network (PANet) have been implemented that results in superior detection under various challenging environments. Results. Evaluating the model performance in a custom endangered wildlife dataset considering high variability and complex backgrounds, WilDect-YOLO obtains a mean average precision (mAP) value of 96.89%, F1-score of 97.87%, and precision value of 97.18% at a detection rate of 59.20 FPS outperforming current state-of-the-art models. Significance. The present research provides an effective and efficient detection framework addressing the shortcoming of existing DL-based wildlife detection models by providing highly accurate species-level localized bounding box prediction. Current work constitutes a step toward a non-invasive, fully automated an-imal observation system in real-time in-field applications.

Automated summary

In the face of climatic instability, ecological disturbances, and human actions, endangered wildlife species are under threat. An up-to-date and accurate detection process is crucial for protecting biodiversity losses, conservation, and ecosystem management. However, current wildlife detection models often lack superior feature extraction capability in complex environments, limiting the development of accurate and reliable models. To address this issue, WilDect-YOLO is proposed as a deep learning-based automated high-performance detection model for real-time endangered wildlife detection. By incorporating residual blocks, DenseNet blocks, Spatial Pyramid Pooling, and modified Path Aggregation Network, WilDect-YOLO achieves superior detection results under challenging environments. Evaluation on a custom endangered wildlife dataset shows that WilDect-YOLO outperforms current state-of-the-art models with a mean average precision of 96.89% and a detection rate of 59.20 FPS. This research provides an effective framework for highly accurate species-level localized bounding box prediction, contributing to the development of non-invasive, fully automated real-time animal observation systems.