通过分块聚合提升无人机图像中小目标检测召回率
Group Evidence Matters: Tiling-based Semantic Gating for Dense Object Detection
- 分块重叠恢复低置信度候选框，构建群体证据
- 空间与语义双门控验证群体，召回率提升9.3%至0.778
- 无需重训练，适合远距离计数等高召回场景
无人机影像中密集小目标常因远距离视角、遮挡和杂乱而被遗漏。本文提出一种检测器无关的后处理框架，将重叠引起的冗余转化为群体证据。首先通过重叠分块恢复低置信度候选框；再利用基于框中心点的DBSCAN进行空间门控，以及基于ResNet-18特征嵌入的DBSCAN进行语义门控，验证群体证据；经受验证的群体进行可控置信度重加权后，通过类别感知NMS融合。在VisDrone数据集上，召回率从0.685提升至0.778（+0.093），精度从0.801降至0.595，F1达0.669。单图后处理平均延迟0.095秒。实验表明该方法具召回优先、精度权衡特性，适用于远场计数与监控等对召回敏感的应用。消融实验证明分块暴露遗漏目标，空间聚类稳定几何结构，语义聚类保证外观一致性，重加权实现与基线的校准整合。框架无需重训练，可兼容现代检测器。未来工作将降低语义门控开销，并引入时序线索扩展方法。
原文摘要 · Abstract (English)
Dense small objects in UAV imagery are often missed due to long-range viewpoints, occlusion, and clutter[cite: 5]. This paper presents a detector-agnostic post-processing framework that converts overlap-induced redundancy into group evidence[cite: 6]. Overlapping tiling first recovers low-confidence candidates[cite: 7]. A Spatial Gate (DBSCAN on box centroids) and a Semantic Gate (DBSCAN on ResNet-18 embeddings) then validates group evidence[cite: 7]. Validated groups receive controlled confidence reweighting before class-aware NMS fusion[cite: 8]. Experiments on VisDrone show a recall increase from 0.685 to 0.778 (+0.093) and a precision adjustment from 0.801 to 0.595, yielding F1=0.669[cite: 9]. Post-processing latency averages 0.095 s per image[cite: 10]. These results indicate recall-first, precision-trade-off behavior that benefits recall-sensitive applications such as far-field counting and monitoring[cite: 10]. Ablation confirms that tiling exposes missed objects, spatial clustering stabilizes geometry, semantic clustering enforces appearance coherence, and reweighting provides calibrated integration with the baseline[cite: 11]. The framework requires no retraining and integrates with modern detectors[cite: 12]. Future work will reduce semantic gating cost and extend the approach with temporal cues[cite: 13].
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