WildMDT-YOLO: A multi-scale wildlife detection model for complex environments
Chao Li, Youbo Pang, Xianhang Liu, Zhipeng Yu, Minchao Sun · Computer Vision and Image Understanding · 2026
The deterioration of the global ecological environment and increasing human activities pose severe threats to wildlife survival, making reliable detection methods crucial for wildlife protection and monitoring. However, existing detection methods often encounter inadequate feature extraction in complex settings such as tree occlusion, strong illumination, and low-light environments. To address this challenge, this paper proposes WildMDT-YOLO, an improved YOLOv8n-based wildlife detection method. The model introduces a Multi-scale Focusing Diffusion Network (MFDN) that enhances contextual information across scales through feature focusing and diffusion mechanisms. A novel detection head employs shared convolutions to reduce model parameters while task alignment and interactive feature extraction improve both classification and localization accuracy. The integration of Deformable Convolutional Networks v3 (DCNv3) and Mixed Local Channel Attention (MLCA) mechanism further enhances adaptability to wildlife species with complex shapes and varying scales. Experimental results show that WildMDT-YOLO achieves 92.6% mean average precision (mAP), a 3.1% increase over baseline YOLOv8n, while reducing parameters by 17.1%. Cross-dataset evaluation on Snapshot Serengeti demonstrates robust generalization capability, achieving 89.2% mAP and maintaining 83.6% small object detection performance despite significant domain shift between ecosystems. This model provides an effective tool for improving monitoring efficiency and accuracy in wildlife conservation.