  Underwater docking is critical to enable the persistent operation of
Autonomous Underwater Vehicles (AUVs). For this, the AUV must be capable of
detecting and localizing the docking station, which is complex due to the
highly dynamic undersea environment. Image-based solutions offer a high
acquisition rate and versatile alternative to adapt to this environment;
however, the underwater environment presents challenges such as low visibility,
high turbidity, and distortion. In addition to this, field experiments to
validate underwater docking capabilities can be costly and dangerous due to the
specialized equipment and safety considerations required to conduct the
experiments. This work compares different deep-learning architectures to
perform underwater docking detection and classification. The architecture with
the best performance is then compressed using knowledge distillation under the
teacher-student paradigm to reduce the network's memory footprint, allowing
real-time implementation. To reduce the simulation-to-reality gap, a Generative
Adversarial Network (GAN) is used to do image-to-image translation, converting
the Gazebo simulation image into a realistic underwater-looking image. The
obtained image is then processed using an underwater image formation model to
simulate image attenuation over distance under different water types. The
proposed method is finally evaluated according to the AUV docking success rate
and compared with classical vision methods. The simulation results show an
improvement of 20% in the high turbidity scenarios regardless of the underwater
currents. Furthermore, we show the performance of the proposed approach by
showing experimental results on the off-the-shelf AUV Iver3.
