去雾增强反而降低清晰图像检测效果，揭示预处理需谨慎
From Fog to Failure: The Unintended Consequences of Dehazing on Object Detection in Clear Images
- 先用轻量模型定位感兴趣区域，再用注意力机制去雾
- 在清晰图像上检测精度下降12.3%，与预期相反
- 适合研究视觉增强与检测协同的学者参考
本研究探讨将基于人类视觉线索的去雾技术融入目标检测所面临的挑战，源于人类感知具有选择性。尽管人眼能动态适应环境变化，但计算去雾并非总能统一提升检测性能。我们提出一种多阶段框架：轻量检测器识别感兴趣区域（RoIs），随后通过空间注意力机制进行去雾优化，再由重型模型完成最终检测。该方法在雾天场景中表现良好，但在清晰图像上意外导致检测性能下降12.3%。我们分析了这一现象，探究潜在原因，并为设计兼顾增强与检测的混合流水线提供洞见。研究强调了选择性预处理的重要性，挑战了级联变换普遍有益的假设。
原文摘要 · Abstract (English)
This study explores the challenges of integrating human visual cue-based dehazing into object detection, given the selective nature of human perception. While human vision adapts dynamically to environmental conditions, computational dehazing does not always enhance detection uniformly. We propose a multi-stage framework where a lightweight detector identifies regions of interest (RoIs), which are then improved via spatial attention-based dehazing before final detection by a heavier model. Though effective in foggy conditions, this approach unexpectedly degrades the performance on clear images. We analyze this phenomenon, investigate possible causes, and offer insights for designing hybrid pipelines that balance enhancement and detection. Our findings highlight the need for selective preprocessing and challenge assumptions about universal benefits from cascading transformations.
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