Tiny object detection is one of the key challenges in the field of object detection. The performance of most generic detectors dramatically decreases in tiny object detection tasks. The main challenge lies in extracting effective features of tiny objects. Existing methods usually perform generation-based feature enhancement, which is seriously affected by spurious textures and artifacts, making it difficult to make the tiny-object-specific features visible and clear for detection. To address this issue, we propose a self-reconstructed tiny object detection (SR-TOD) framework. We for the first time introduce a self-reconstruction mechanism in the detection model, and discover the strong correlation between it and the tiny objects. Specifically, we impose a reconstruction head in-between the neck of a detector, constructing a difference map of the reconstructed image and the input, which shows high sensitivity to tiny objects. This inspires us to enhance the weak representations of tiny objects under the guidance of the difference maps. Thus, improving the visibility of tiny objects for the detectors. Building on this, we further develop a Difference Map Guided Feature Enhancement (DGFE) module to make the tiny feature representation more clear. In addition, we further propose a new multi-instance anti-UAV dataset, which is called DroneSwarms dataset and contains a large number of tiny drones with the smallest average size to date. Extensive experiments on the DroneSwarms dataset and other datasets demonstrate the effectiveness of the proposed method. The code and dataset will be publicly available.

翻译：微小目标检测是目标检测领域的关键挑战之一。大多数通用检测器在微小目标检测任务中性能显著下降。主要挑战在于提取微小目标的有效特征。现有方法通常采用基于生成的特征增强技术，但易受虚假纹理和伪影的严重影响，难以使微小目标的特异性特征在检测中变得可见且清晰。为解决这一问题，我们提出了一种自重构微小目标检测框架。我们首次在检测模型中引入自重构机制，并发现其与微小目标之间存在强相关性。具体而言，我们在检测器颈部嵌入重构头，构建重构图像与输入图像的差异图，该差异图对微小目标表现出高度敏感性。这启发我们在差异图的引导下增强微小目标的弱表征，从而提升检测器对微小目标的可见性。在此基础上，我们进一步开发了差异图引导特征增强模块，使微小特征表征更加清晰。此外，我们提出了一个新的多实例反无人机数据集，命名为DroneSwarms数据集，其中包含大量迄今平均尺寸最小的微型无人机。在DroneSwarms数据集及其他数据集上的大量实验证明了所提方法的有效性。代码与数据集将公开提供。