用EfficientNetB4实现空中目标分类与威胁等级预测，准确率超90%。
Towards Airborne Object Detection: A Deep Learning Analysis
- 基于EfficientNetB4设计双任务模型，同时完成分类与威胁评估。
- 在新构建的AODTA数据集上达96%分类准确率、90%威胁预测准确率。
- 适合安防、空管等需实时空中目标研判的场景。
随着商用飞机、无人机及无人飞行器的迅速普及，对实时自动化威胁评估系统的需求日益迫切。现有方法依赖人工监控，难以扩展且效率低下。本文提出一种基于EfficientNetB4的双任务模型，可同步完成空中目标分类与威胁等级预测。为解决高质量、平衡训练数据稀缺问题，我们整合并清洗多个公开数据源，构建了AODTA数据集。在AVD数据集与新构建的AODTA数据集上进行基准测试，并与ResNet-50基线对比，结果表明EfficientNetB4性能更优。该模型在物体分类任务中达到96%准确率，在威胁等级预测中达到90%准确率，展现出在监控、国防与空域管理中的应用潜力。尽管标题提及检测，但本研究聚焦于使用已有数据集中预定位的空中目标图像进行分类与威胁推理。
原文摘要 · Abstract (English)
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.
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