English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

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

Reveal of Domain Effect: How Visual Restoration Contributes to Object Detection in Aquatic Scenes