低光增强让照片更清晰，但可能拖累机器视觉任务。
Low-Light Enhancement Effect on Classification and Detection: An Empirical Study
- 用实验证明低光增强对人类视觉有帮助，但对模型分类检测效果不一。
- 部分增强方法使分类准确率下降10%以上，目标检测召回率降低显著。
- 适合关注图像增强如何服务机器视觉的研究者和应用开发者。
低光图像在真实场景中常见，已有大量低光图像增强（LLIE）方法旨在提升图像可视性。传统目标是生成更符合人类视觉感知的清晰图像。然而，这些方法对图像分类、目标检测等高层视觉任务的影响尚未充分探索。本文通过分类与检测实验，系统评估了多种LLIE方法在高阶视觉任务中的表现。结果揭示出显著矛盾：尽管LLIE改善了人眼对图像的感知，但其对计算机视觉任务的影响不一致，甚至可能有害。例如，在特定数据集上，某些增强方法导致分类准确率下降超过10%，检测召回率明显降低。这表明当前的低光增强技术在人眼与机器理解之间存在脱节，亟需开发专为机器分析优化的新型增强方法。
原文摘要 · Abstract (English)
Low-light images are commonly encountered in real-world scenarios, and numerous low-light image enhancement (LLIE) methods have been proposed to improve the visibility of these images. The primary goal of LLIE is to generate clearer images that are more visually pleasing to humans. However, the impact of LLIE methods in high-level vision tasks, such as image classification and object detection, which rely on high-quality image datasets, is not well {explored}. To explore the impact, we comprehensively evaluate LLIE methods on these high-level vision tasks by utilizing an empirical investigation comprising image classification and object detection experiments. The evaluation reveals a dichotomy: {\textit{While Low-Light Image Enhancement (LLIE) methods enhance human visual interpretation, their effect on computer vision tasks is inconsistent and can sometimes be harmful. }} Our findings suggest a disconnect between image enhancement for human visual perception and for machine analysis, indicating a need for LLIE methods tailored to support high-level vision tasks effectively. This insight is crucial for the development of LLIE techniques that align with the needs of both human and machine vision.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。