Existing instance segmentation techniques are primarily tailored for high-visibility inputs, but their performance significantly deteriorates in extremely low-light environments. In this work, we take a deep look at instance segmentation in the dark and introduce several techniques that substantially boost the low-light inference accuracy. The proposed method is motivated by the observation that noise in low-light images introduces high-frequency disturbances to the feature maps of neural networks, thereby significantly degrading performance. To suppress this ``feature noise", we propose a novel learning method that relies on an adaptive weighted downsampling layer, a smooth-oriented convolutional block, and disturbance suppression learning. These components effectively reduce feature noise during downsampling and convolution operations, enabling the model to learn disturbance-invariant features. Furthermore, we discover that high-bit-depth RAW images can better preserve richer scene information in low-light conditions compared to typical camera sRGB outputs, thus supporting the use of RAW-input algorithms. Our analysis indicates that high bit-depth can be critical for low-light instance segmentation. To mitigate the scarcity of annotated RAW datasets, we leverage a low-light RAW synthetic pipeline to generate realistic low-light data. In addition, to facilitate further research in this direction, we capture a real-world low-light instance segmentation dataset comprising over two thousand paired low/normal-light images with instance-level pixel-wise annotations. Remarkably, without any image preprocessing, we achieve satisfactory performance on instance segmentation in very low light (4~\% AP higher than state-of-the-art competitors), meanwhile opening new opportunities for future research.

翻译：现有实例分割技术主要针对高可见度输入设计，但在极低光照条件下其性能会显著下降。本文深入研究了暗光环境下的实例分割问题，并提出多项可大幅提升低光照推理准确率的技术。所提方法的动机源于观察到：低光照图像中的噪声会向神经网络特征图引入高频扰动，从而显著降低性能。为抑制这种“特征噪声”，我们提出一种基于自适应加权下采样层、平滑导向卷积块及扰动抑制学习的新型学习方法。这些组件能有效降低下采样与卷积运算过程中的特征噪声，使模型学习到扰动不变性特征。此外，我们发现高比特深度RAW图像相比典型相机sRGB输出能更完整保留低光照场景信息，从而支持RAW输入算法。分析表明，高比特深度对低光照实例分割至关重要。为缓解标注RAW数据集的稀缺性，我们利用低光照RAW合成管线生成逼真的低光照数据。同时，为促进该方向的后续研究，我们采集了包含两千余对低/正常光照图像且配备实例级像素标注的真实世界低光照实例分割数据集。值得注意的是，无需任何图像预处理，我们即可在极低光照条件下实现令人满意的实例分割性能（比当前最优方法高4% AP），同时为未来研究开辟新方向。