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<p>Robust object detection in haze-degraded unmanned aerial vehicle imagery remains challenging because atmospheric corruption progressively weakens neural representations throughout the detection pipeline, while the aerial viewpoint further intensifies scale variation and small-object ambiguity. Existing solutions usually depend on image dehazing, external priors, or heavy multimodal systems, which often suffer from object–restoration mismatch, limited robustness, or excessive complexity. In this paper, we present the Multi-stage Purified Representation Network (MPRNet), a lightweight end-to-end detection framework that improves degraded-scene representation learning through architecture-level feature purification. The proposed network performs semantic purification in deep representations and multi-scale purification during cross-scale feature interaction, thereby reducing haze-related noise propagation and improving the recovery of weak small-object cues. Experimental results on three hazy-scene benchmarks show that MPRNet achieves competitive detection accuracy and consistent performance across both synthetic and real hazy scenes. On the main benchmark, MPRNet achieves 55.3% mean average precision, improving the strongest competing method by 3.3 percentage points while requiring only 10.3 million parameters and 30.9 billion floating-point operations. Edge-side evaluation on Jetson Xavier NX further reports 33.5–36.1 milliseconds of inference latency together with low memory occupancy, providing supplementary evidence that the proposed model offers a favorable accuracy–efficiency trade-off and is suitable for edge deployment.</p>

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Multi-stage feature purification for robust object detection in haze-degraded aerial images

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月10日

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  • Xiaorong Zhang,
  • Xuting Hu

摘要

Robust object detection in haze-degraded unmanned aerial vehicle imagery remains challenging because atmospheric corruption progressively weakens neural representations throughout the detection pipeline, while the aerial viewpoint further intensifies scale variation and small-object ambiguity. Existing solutions usually depend on image dehazing, external priors, or heavy multimodal systems, which often suffer from object–restoration mismatch, limited robustness, or excessive complexity. In this paper, we present the Multi-stage Purified Representation Network (MPRNet), a lightweight end-to-end detection framework that improves degraded-scene representation learning through architecture-level feature purification. The proposed network performs semantic purification in deep representations and multi-scale purification during cross-scale feature interaction, thereby reducing haze-related noise propagation and improving the recovery of weak small-object cues. Experimental results on three hazy-scene benchmarks show that MPRNet achieves competitive detection accuracy and consistent performance across both synthetic and real hazy scenes. On the main benchmark, MPRNet achieves 55.3% mean average precision, improving the strongest competing method by 3.3 percentage points while requiring only 10.3 million parameters and 30.9 billion floating-point operations. Edge-side evaluation on Jetson Xavier NX further reports 33.5–36.1 milliseconds of inference latency together with low memory occupancy, providing supplementary evidence that the proposed model offers a favorable accuracy–efficiency trade-off and is suitable for edge deployment.