The rapid proliferation of airborne platforms, including commercial aircraft, drones, and UAVs, has intensified the need for real-time, automated threat assessment systems. Current approaches depend heavily on manual monitoring, resulting in limited scalability and operational inefficiencies. This work introduces a dual-task model based on EfficientNetB4 capable of performing airborne object classification and threat-level prediction simultaneously. To address the scarcity of clean, balanced training data, we constructed the AODTA Dataset by aggregating and refining multiple public sources. We benchmarked our approach on both the AVD Dataset and the newly developed AODTA Dataset and further compared performance against a ResNet-50 baseline, which consistently underperformed EfficientNetB4. Our EfficientNetB4 model achieved 96% accuracy in object classification and 90% accuracy in threat-level prediction, underscoring its promise for applications in surveillance, defense, and airspace management. Although the title references detection, this study focuses specifically on classification and threat-level inference using pre-localized airborne object images provided by existing datasets.

翻译：随着商业飞机、无人机和无人飞行器等空中平台的迅速普及，对实时自动化威胁评估系统的需求日益迫切。当前方法严重依赖人工监控，导致可扩展性有限且运行效率低下。本研究提出了一种基于EfficientNetB4的双任务模型，能够同时执行空中目标分类与威胁等级预测。为解决清洁、平衡训练数据稀缺的问题，我们通过整合与精炼多个公开数据源构建了AODTA数据集。我们在AVD数据集与新开发的AODTA数据集上对本方法进行了基准测试，并进一步与ResNet-50基线模型进行性能比较，后者在所有测试中均表现逊色于EfficientNetB4模型。我们的EfficientNetB4模型在目标分类任务中达到96%准确率，在威胁等级预测任务中达到90%准确率，彰显了其在监控、防御与空域管理领域的应用潜力。尽管标题提及检测，本研究实际聚焦于利用现有数据集提供的预定位空中目标图像进行分类与威胁等级推断。