Toward Precision Biodiversity Detection: An Edge‐Deployable Framework for Mitigating Data Redundancy

Monitoring wildlife in remote areas is vital for biodiversity research, yet infrared‐triggered cameras often produce large numbers of false or empty images due to environmental interference, causing data overload and power waste. To address this, this manuscript proposes WS‐YOLO, a lightweight edge‐optimized wildlife detection model based on improved YOLOv11n, which aims to integrate false‐trigger filtering and accurate animal detection in a single model for field applications. Specifically, the model introduces three key enhancements: it adopts a Wavelet Convolution (WTConv) module to capture multi‐scale and frequency‐aware features, boosting robustness to complex backgrounds and small‐target detection accuracy; meanwhile, a Spatial‐Channel Synergistic Attention (SCSA) mechanism is incorporated to enhance spatial localization and channel‐wise semantic encoding, enabling the model to focus on salient regions of wildlife; additionally, a SlideLoss function is designed to prioritize training on boundary cases, improving the handling of ambiguous detection instances. Experiments on four wildlife datasets demonstrate that WS‐YOLO achieves excellent mAP@0.50 performance: 93.0% on African Wildlife, 98.0% on Amur Tiger, 98.5% on the NTLNP infrared dataset, and 79.4% on PASCAL VOC, confirming its robustness across diverse scenarios. Deployment on a Jetson Nano further validates its feasibility for real‐time, energy‐efficient use in the field. WS‐YOLO enables accurate animal recognition and classification directly at edge devices, significantly reducing communication load and enhancing monitoring efficiency.