In recent years, more concerns are shed on the lightweight detection model in remote sensing (RS), but it is difficult to reach a competitive performance relative to the deep model. Knowledge distillation has been verified as a promising method, which can promote the performance of the lightweight model without extra parameters. While there are two key issues of detection distillation, one is the sample selection and the other is the knowledge selection. Since the varying object size and complex features in RS, the existing methods based on the fixed threshold are incapable of selecting the optimal distillation samples and they also ignore the potential multivariate knowledge among RS samples simultaneously. In this article, we propose a statistical sample selection and multivariate knowledge mining framework. The statistical sample selection module formulates the task as the modeling and splitting of the probability distribution of sample selection cost, which is more suitable for dynamically choosing multiscale samples in RS and eliminates the distortion of previous static distillation selection. Furthermore, to mine the complex feature knowledge of samples in RS, we design a multivariate knowledge mining module, in which knowledge includes explicit and implicit knowledge. The proposed module validly delivers the core knowledge from the teacher model to the lightweight model. Massive experiments on three challenging RS datasets [a large-scale Dataset for Object deTection in Aerial images (DOTA), Northwestern Polytechnical University very-high-resolution 10-class (NWPU VHR-10), and object DetectIon in Optical Remote sensing images (DIOR)] prove that our method achieves state-of-the-art performance.

中文翻译：

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遥感影像中轻量级探测器的统计样本选择与多元知识挖掘

近年来，遥感（RS）中的轻量级检测模型越来越受到关注，但相对于深度模型很难达到具有竞争力的性能。知识蒸馏已被证明是一种很有前途的方法，它可以在没有额外参数的情况下提升轻量级模型的性能。检测蒸馏有两个关键问题，一个是样本选择，另一个是知识选择。由于RS中目标大小的变化和复杂的特征，现有的基于固定阈值的方法无法选择最佳蒸馏样本，同时也忽略了RS样本之间潜在的多元知识。在本文中，我们提出了一个统计样本选择和多元知识挖掘框架。统计样本选择模块将任务表述为样本选择成本概率分布的建模和拆分，更适合在RS中动态选择多尺度样本，消除了之前静态蒸馏选择的失真。此外，为了挖掘RS中样本的复杂特征知识，我们设计了一个多元知识挖掘模块，其中知识包括显性知识和隐性知识。所提出的模块有效地将核心知识从教师模型传递到轻量级模型。对三个具有挑战性的 RS 数据集进行大规模实验 [a large-scale Dataset for Object detection in Aerial images (DOTA)，西北工业大学超高分辨率 10 级 (NWPU VHR-10)，