  Underwater robotic perception usually requires visual restoration and object
detection, both of which have been studied for many years. Meanwhile, data
domain has a huge impact on modern data-driven leaning process. However,
exactly indicating domain effect, the relation between restoration and
detection remains unclear. In this paper, we generally investigate the relation
of quality-diverse data domain to detection performance. In the meantime, we
unveil how visual restoration contributes to object detection in real-world
underwater scenes. According to our analysis, five key discoveries are
reported: 1) Domain quality has an ignorable effect on within-domain
convolutional representation and detection accuracy; 2) low-quality domain
leads to higher generalization ability in cross-domain detection; 3)
low-quality domain can hardly be well learned in a domain-mixed learning
process; 4) degrading recall efficiency, restoration cannot improve
within-domain detection accuracy; 5) visual restoration is beneficial to
detection in the wild by reducing the domain shift between training data and
real-world scenes. Finally, as an illustrative example, we successfully perform
underwater object detection with an aquatic robot.
