  Dark environment becomes a challenge for computer vision algorithms owing to
insufficient photons and undesirable noise. To enhance object detection in a
dark environment, we propose a novel multitask auto encoding transformation
(MAET) model which is able to explore the intrinsic pattern behind illumination
translation. In a self-supervision manner, the MAET learns the intrinsic visual
structure by encoding and decoding the realistic illumination-degrading
transformation considering the physical noise model and image signal processing
(ISP).
  Based on this representation, we achieve the object detection task by
decoding the bounding box coordinates and classes. To avoid the
over-entanglement of two tasks, our MAET disentangles the object and degrading
features by imposing an orthogonal tangent regularity. This forms a parametric
manifold along which multitask predictions can be geometrically formulated by
maximizing the orthogonality between the tangents along the outputs of
respective tasks. Our framework can be implemented based on the mainstream
object detection architecture and directly trained end-to-end using normal
target detection datasets, such as VOC and COCO. We have achieved the
state-of-the-art performance using synthetic and real-world datasets. Code is
available at https://github.com/cuiziteng/MAET.
