Small object detection in large-scale remote sensing images (RSIs) is crucial for military and civil applications, but it remains challenging. Since small objects occupy few pixels, their features are easily interfered with by complex backgrounds and large objects. In addition, they are susceptible to localization offsets, which are prone to false or missed detections as there are few predicted bounding boxes matching the ground truth. To overcome these issues, this article proposes a filter progressive small object detection (FPSOD) model that is based on the progressive mechanism. With the proposed attention-based soft-threshold filtering module, FPSOD significantly filters out redundant information in high-level feature maps thus enhancing the semantic features of small objects. Furthermore, a progressive regression loss (PR-Loss) function is proposed to facilitate the precise localization, which mitigates predicted bounding box drift by limiting the fluctuated range of the gradients. The experimental results show that the proposed model substantially improves the precision and recall of small objects, effectively reduces missed detections, and improves detection performance.

中文翻译：

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基于冗余特征去除和渐进回归的遥感图像小目标检测


大规模遥感图像（RSI）中的小物体检测对于军事和民用应用至关重要，但仍然具有挑战性。由于小物体占用的像素很少，因此它们的特征很容易受到复杂背景和大物体的干扰。此外，它们很容易受到定位偏移的影响，这很容易出现错误或漏检，因为很少有预测的边界框与地面实况相匹配。为了克服这些问题，本文提出了一种基于渐进机制的滤波器渐进小目标检测（FPSOD）模型。通过提出的基于注意力的软阈值过滤模块，FPSOD显着过滤掉高级特征图中的冗余信息，从而增强小对象的语义特征。此外，提出了渐进回归损失（PR-Loss）函数来促进精确定位，通过限制梯度的波动范围来减轻预测的边界框漂移。实验结果表明，该模型大幅提高了小物体的查准率和查全率，有效减少了漏检，提高了检测性能。