稀缺数据机制下的航空图像目标检测
Object Detection in Aerial Images in Scarce Data Regimes
AI总结：
本研究针对航空图像中的少样本目标检测，发现小物体导致性能差距，提出注意力机制和尺度自适应框相似性准则，并探索跨域FSOD及在COSE系统中的实时部署。
摘要：
大多数关于少样本目标检测（FSOD）的贡献仅在自然图像上评估其方法，然而所宣称性能的可迁移性对于其他类型图像上的应用并不能得到保证。我们通过对现有FSOD方法在航空图像上的深入分析证明了这一点，并观察到与自然图像相比存在巨大的性能差距。航空图像中数量更多的小物体是自然图像与航空图像之间明显性能差距的原因。因此，我们通过精心设计的注意力机制提高了FSOD在小物体上的性能。此外，我们还提出了一种尺度自适应的框相似性准则，该准则改进了FSOD方法的训练和评估，特别是对于小物体。我们还通过基于度量学习和微调的两种不同方法为通用FSOD做出了贡献。微调方法取得了令人印象深刻的结果，这鼓励了解决更复杂场景（如跨域FSOD）的尝试。我们在这个方向上进行了初步实验，并获得了有希望的结果。最后，我们解决了检测模型在COSE系统内部的部署问题。检测必须在超过100兆像素的极大图像上实时进行，且计算能力有限。利用TensorRT等现有优化工具，我们成功应对了这一工程挑战。
英文摘要：
Most contributions on Few-Shot Object Detection (FSOD) evaluate their methods on natural images only, yet the transferability of the announced performance is not guaranteed for applications on other kinds of images. We demonstrate this with an in-depth analysis of existing FSOD methods on aerial images and observed a large performance gap compared to natural images. Small objects, more numerous in aerial images, are the cause for the apparent performance gap between natural and aerial images. As a consequence, we improve FSOD performance on small objects with a carefully designed attention mechanism. In addition, we also propose a scale-adaptive box similarity criterion, that improves the training and evaluation of FSOD methods, particularly for small objects. We also contribute to generic FSOD with two distinct approaches based on metric learning and fine-tuning. Impressive results are achieved with the fine-tuning method, which encourages tackling more complex scenarios such as Cross-Domain FSOD. We conduct preliminary experiments in this direction and obtain promising results. Finally, we address the deployment of the detection models inside COSE's systems. Detection must be done in real-time in extremely large images (more than 100 megapixels), with limited computation power. Leveraging existing optimization tools such as TensorRT, we successfully tackle this engineering challenge.
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