To improve detection robustness in adverse conditions (e.g., haze and low light), image restoration is commonly applied as a pre-processing step to enhance image quality for the detector. However, the functional mismatch between restoration and detection networks can introduce instability and hinder effective integration -- an issue that remains underexplored. We revisit this limitation through the lens of Lipschitz continuity, analyzing the functional differences between restoration and detection networks in both the input space and the parameter space. Our analysis shows that restoration networks perform smooth, continuous transformations, while object detectors operate with discontinuous decision boundaries, making them highly sensitive to minor perturbations. This mismatch introduces instability in traditional cascade frameworks, where even imperceptible noise from restoration is amplified during detection, disrupting gradient flow and hindering optimization. To address this, we propose Lipschitz-regularized object detection (LROD), a simple yet effective framework that integrates image restoration directly into the detector's feature learning, harmonizing the Lipschitz continuity of both tasks during training. We implement this framework as Lipschitz-regularized YOLO (LR-YOLO), extending seamlessly to existing YOLO detectors. Extensive experiments on haze and low-light benchmarks demonstrate that LR-YOLO consistently improves detection stability, optimization smoothness, and overall accuracy.

翻译：为提升恶劣条件（如雾霾与低光照）下的检测鲁棒性，图像复原常被用作预处理步骤以增强检测器的输入图像质量。然而，复原网络与检测网络之间的功能失配可能引发不稳定性并阻碍有效集成——这一问题尚未得到充分探索。本文通过Lipschitz连续性的视角重新审视该局限，从输入空间与参数空间两个维度分析复原网络与检测网络的功能差异。分析表明，复原网络执行平滑的连续变换，而目标检测器则基于非连续的决策边界运作，使其对微小扰动高度敏感。这种失配在传统级联框架中引入了不稳定性：即使复原产生的不可察觉噪声也会在检测阶段被放大，破坏梯度流并阻碍优化。为解决此问题，我们提出Lipschitz正则化目标检测（LROD），该框架将图像复原直接集成至检测器的特征学习中，在训练过程中协调两项任务的Lipschitz连续性。我们将该框架实现为Lipschitz正则化YOLO（LR-YOLO），可无缝扩展至现有YOLO检测器。在雾霾与低光照基准数据集上的大量实验表明，LR-YOLO能持续提升检测稳定性、优化平滑度与整体精度。