Group Evidence Matters: Tiling-based Semantic Gating for Dense Object Detection

• URL: http://arxiv.org/abs/2509.10779v2
• Date: Fri, 26 Sep 2025 17:22:22 GMT
• Title: Group Evidence Matters: Tiling-based Semantic Gating for Dense Object Detection
• Abstract summary: This paper presents a detector-agnostic post-processing framework that converts overlap-induced redundancy into group evidence.<n>The framework requires no retraining and integrates with modern detectors.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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