Object detection in low-light conditions remains a challenging but important
problem with many practical implications. Some recent works show that, in
low-light conditions, object detectors using raw image data are more robust
than detectors using image data processed by a traditional ISP pipeline. To
improve detection performance in low-light conditions, one can fine-tune the
detector to use raw image data or use a dedicated low-light neural pipeline
trained with paired low- and normal-light data to restore and enhance the
image. However, different camera sensors have different spectral sensitivity
and learning-based models using raw images process data in the sensor-specific
color space. Thus, once trained, they do not guarantee generalization to other
camera sensors. We propose to improve generalization to unseen camera sensors
by implementing a minimal neural ISP pipeline for machine cognition, named
GenISP, that explicitly incorporates Color Space Transformation to a
device-independent color space. We also propose a two-stage color processing
implemented by two image-to-parameter modules that take down-sized image as
input and regress global color correction parameters. Moreover, we propose to
train our proposed GenISP under the guidance of a pre-trained object detector
and avoid making assumptions about perceptual quality of the image, but rather
optimize the image representation for machine cognition. At the inference
stage, GenISP can be paired with any object detector. We perform extensive
experiments to compare our method to other low-light image restoration and
enhancement methods in an extrinsic task-based evaluation and validate that
GenISP can generalize to unseen sensors and object detectors. Finally, we
contribute a low-light dataset of 7K raw images annotated with 46K bounding
boxes for task-based benchmarking of future low-light image restoration and
object detection.

在低光条件下进行物体检测仍然是一个具有许多实际应用的具有挑战性但重要的问题。我们提出了一种名为 GenISP 的最小神经 ISP 管线用于机器认知，它将颜色空间转换明确地融入到独立于设备的颜色空间中，以提高对未见的相机传感器的泛化性，同时与任何物体检测器配对。