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February 2, 2026Open Access

RAPT-Net: Reliability-Aware Precision-Preserving Tolerance-Enhanced Network for Tiny Target Detection in Wide-Area Coverage Aerial Remote Sensing

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Authors

PZPeida ZhouXGXiaojun GuoXSXiaoyong Sun

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Overview

Demonstrates enhanced detection of tiny targets in aerial remote sensing, implying improved surveillance capabilities.

Key Points

  • This research aims to improve detection accuracy of small objects in aerial remote sensing environments.
  • Introduced RAPT-Net with three core modules for enhanced detection.
  • MRAAF for scene-adaptive modality integration.
  • CMFE-SRP for balancing spatial detail and semantic information.
  • DS-STD to increase positive sample coverage significantly.
  • Achieved mAP values of 62.22% on VEDAI and 18.52% on RGBT-Tiny.
  • Improved state-of-the-art performance by 4.3% and 10.3%, respectively.
  • Enhanced detection capabilities for extremely tiny targets by 17.3%.

Cite This Study

Zhou et al. (2026) studied this question.

synapsesocial.com/papers/69810013c1c9540dea81326dhttps://doi.org/10.3390/rs18030449
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1AeroDetectNet: a lightweight, high-precision network for enhanced detection of small objects in aerial remote sensing imagery2024 · 15 citations
  2. 2Resolution Preserving and Utilization Network for Tiny Object Detection in Large-Size Remote Sensing Imagery2026 · 2 citations
  3. 3RS-TinyNet: Stage-wise Feature Fusion Network for Detecting Tiny Objects in Remote Sensing Images2025
  4. 4Aero-LiteNet: robust aerial small object detection via multi-scale fusion and neighborhood-aware optimization2026
  5. 5A Multiscale Feature Enhancement and Adaptive Perception Network for Object Detection in Remote Sensing Image2025 · 3 citations