  Images acquired by computer vision systems under low light conditions have
multiple characteristics like high noise, lousy illumination, reflectance, and
bad contrast, which make object detection tasks difficult. Much work has been
done to enhance images using various pixel manipulation techniques, as well as
deep neural networks - some focused on improving the illumination, while some
on reducing the noise. Similarly, considerable research has been done in object
detection neural network models. In our work, we break down the problem into
two phases: 1)First, we explore which image enhancement algorithm is more
suited for object detection tasks, where accurate feature retrieval is more
important than good image quality. Specifically, we look at basic histogram
equalization techniques and unpaired image translation techniques. 2)In the
second phase, we explore different object detection models that can be applied
to the enhanced image. We conclude by comparing all results, calculating mean
average precisions (mAP), and giving some directions for future work.
