面向退化红外小目标检测的退化自适应物理引导恢复方法
Degraded Infrared Small Object Detection via Degradation-Adapted Physics-Guided Restoration
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中文总结 AI 辅助
针对红外小目标检测中退化泛化性差的问题，提出DAISOD框架，结合退化识别、专用分支处理与物理引导恢复，构建对应数据集，实验表明其性能优于现有方法。
中文摘要 AI 辅助
近年来红外小目标检测已取得显著进展，但雾、非均匀性等退化会抑制目标-背景对比度，大幅提升检测难度。现有方法主要将图像恢复作为预处理步骤，但通常针对特定退化类型设计，无法泛化到不同退化场景。为缓解该问题，本文提出DAISOD，一种用于在不同退化下实现鲁棒检测的退化自适应红外小目标检测框架。DAISOD首先识别退化的类型与严重程度，随后通过专用分支调整处理方式，最终融合结果用于后续检测。此外，该框架融入了物理引导恢复机制，通过物理模型显式估计退化参数并消除退化影响，避免过度恢复可能导致的小目标丢失。同时，本文构建了涵盖多种退化类型与等级的退化红外小目标检测数据集。大量实验表明，DAISOD在各类退化条件下均优于现有最先进方法。
英文摘要
Infrared small object detection has made significant progress in recent years. However, degradations such as fog and nonuniformity can suppress target-background contrast, substantially increasing detection difficulty. Existing methods mainly rely on image restoration as preprocessing, but they are typically designed for specific degradation types and fail to generalize to varying degradations. To alleviate this, we propose DAISOD, a degradation-adapted infrared small object detection framework for robust detection under different degradations. DAISOD first identifies the type and severity of degradations, then adapts the processing via dedicated branches, and finally fuses the results for subsequent detection. Moreover, a physics-guided restoration mechanism is incorporated to explicitly estimate degradation parameters and remove degradation effects through physical models, avoiding excessive restoration that may erase small targets. Moreover, we construct a degraded infrared small object detection dataset covering diverse degradation types and levels. Extensive experiments show that DAISOD outperforms state-of-the-art methods under various degradation conditions.
发表机构
- School of Artificial Intelligence and Automation, Huazhong University of Science and Technology（华中科技大学人工智能与自动化学院）
- School of Ocean Engineering, Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院海洋工程学院）
机构由 AI 辅助整理，请以论文原文为准。
