低光图像增强与检测联合优化，提升暗光场景目标检测精度
AMIEOD: Adaptive Multi-Experts Image Enhancement for Object Detection in Low-Illumination Scenes
- 多专家增强模块融合多种图像增强策略
- 检测引导回归损失提升增强结果与检测任务匹配度
- 动态选择最优增强策略，适合暗光目标检测应用
在多媒体应用场景中，低光照条件下捕获的图像往往导致视觉感知任务精度下降。为此，本文提出AMIEOD框架，将图像增强与目标检测联合优化，以提升暗光场景下的检测性能。核心包括：多专家图像增强模块（MEIEM），融合多种增强策略以充分挖掘低光图像信息；检测引导回归损失（DGRL），利用检测结果确定回归目标，使增强更贴合检测需求；以及由检测引导交叉熵损失（DGCE）驱动的专家选择模块（ESM），将专家选择建模为分类任务，推理时动态选择最优增强策略。该方法与现有检测算法兼容，显著提升暗光场景下的检测精度。在多个数据集上的实验验证了其有效性。代码已开源。
原文摘要 · Abstract (English)
In multimedia application scenarios, images captured under low-illumination conditions often lead to lower accuracy in visual perception tasks compared to those taken in well-lit environments. To tackle this challenge, we propose AMIEOD, an image enhancement-enabled object detection framework for low-illumination scenes, where the two tasks are jointly optimized in a detection performance-oriented manner. Specifically, to fully exploit the information in poorly lit images, a Multi-Experts Image Enhancement Module (MEIEM) is proposed, which leverages diverse enhancement strategies. On this basis, aiming to better align the MEIEM with the detection task, we propose a Detection-Guided Regression Loss (DGRL) that utilizes the detection result to decide the regression target. Moreover, to dynamically select the most suitable enhancement strategy from MEIEM during inference, we construct an Expert Selection Module (ESM) guided by the proposed Detection-Guided Cross-Entropy (DGCE) loss, which formulates the optimization of ESM as a classification task. The improved method is well-matched with current detection algorithms to improve their performance in dim scenes. Extensive experiments on multiple datasets demonstrate that the proposed method significantly improves object detection accuracy in low-illumination conditions. Our code has been released at https://github.com/scujayfantasy/AMIEOD
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