随着国家公园内红外相机、无人机等设备的大规模应用,生物多样性等监测数据呈爆炸式增长,而传统人工识别方式效率低,难以满足大数据时代监测需求,因此推动智能化识别技术成为必然选择。为实现低算力、多任务场景的系统化模型选型与集成,构建模块化模型训练与部署框架,在国家公园真实场景数据集上系统对比MobileNetV3、EfficientNetB0和EfficientNetB3的性能。结果表明:1)MobileNetV3适用于边缘部署,EfficientNetB0适配中等算力场景,EfficientNetB3适用于高精度离线分析。2)将MobileNetV3部署于大熊猫国家公园卧龙片区红外相机终端,进行优化微调后,验证准确率提升至89.2%,误报率降至3.8%,验证了轻量化模型在野外环境中的可行性与鲁棒性。该框架支持增量学习与多源数据接入,为国家公园智能监测提供了可复用的工程化方案。

With the large-scale application of devices such as infrared cameras and unmanned aerial vehicles in national parks,monitoring data on biodiversity has grown explosively.Traditional manual identification methods are inefficient and difficult to meet the monitoring needs of the big data era,making the promotion of intelligent recognition technology an inevitable choice.To achieve systematic model selection and integration for low-computing-power and multi-task scenarios,this study constructs a modular model training and deployment framework,and systematically compares the performance of MobileNetV3,EfficientNetB0,and EfficientNetB3 on a real-world dataset from national parks.1)MobileNetV3 is most suitable for edge deployment,EfficientNetB0 is appropriate for medium-computing-power scenarios,and EfficientNetB3 is applicable for high-precision offline analysis.2)After deploying MobileNetV3 on infrared camera terminals in the Wolong Area of the Giant Panda National Park and performing optimization and fine-tuning,the validation accuracy increased to 89.2%,and the false positive rate decreased to 3.8%,which verified the feasibility and robustness of lightweight models in field environments.The framework supports incremental learning and multi-source data integration,providing a reusable engineering solution for intelligent monitoring in national parks.