  In this paper, we introduce a generative model for image enhancement
specifically for improving diver detection in the underwater domain. In
particular, we present a model that integrates generative adversarial network
(GAN)-based image enhancement with the diver detection task. Our proposed
approach restructures the GAN objective function to include information from a
pre-trained diver detector with the goal to generate images which would enhance
the accuracy of the detector in adverse visual conditions. By incorporating the
detector output into both the generator and discriminator networks, our model
is able to focus on enhancing images beyond aesthetic qualities and
specifically to improve robotic detection of scuba divers. We train our network
on a large dataset of scuba divers, using a state-of-the-art diver detector,
and demonstrate its utility on images collected from oceanic explorations of
human-robot teams. Experimental evaluations demonstrate that our approach
significantly improves diver detection performance over raw, unenhanced images,
and even outperforms detection performance on the output of state-of-the-art
underwater image enhancement algorithms. Finally, we demonstrate the inference
performance of our network on embedded devices to highlight the feasibility of
operating on board mobile robotic platforms.
