Effective monitoring of protected wildlife is crucial for biodiversity conservation. While camera traps provide valuable data for ecological observation, existing deep learning models often suffer from low accuracy in detecting rare species and high computational costs, hindering their deployment on edge devices. To address these challenges, this study proposes YOLO11-APS, an improved lightweight model for protected wildlife detection. It enhances the YOLO11n by integrating the self-Attention and Convolution (ACmix) module, the Partial Convolution (PConv) module, and the SlimNeck paradigm. These improvements strengthen feature extraction under complex conditions while reducing computational costs. Experimental results demonstrate that YOLO11-APS achieves superior detection performance compared to the baseline model, attaining a precision of 92.7%, a recall of 87.0%, an mAP@0.5 of 92.6% and an mAP@0.5:0.95 of 62.2%. In terms of model lightweighting, YOLO11-APS reduces the number of parameters, floating-point operations, and model size by 10.1%, 11.1%, and 9.5%, respectively. YOLO11-APS achieves an optimal balance between accuracy and model complexity, outperforming existing mainstream lightweight detection models. Furthermore, tests on unseen wildlife data confirm its strong transferability and robustness. This work provides an efficient deep learning tool for automated wildlife monitoring in protected areas, facilitating the development of intelligent ecological sensing systems.
Keywords: YOLO; camera traps; lightweight deep learning; object detection; protected wildlife.