论文标题
揭示域效应：视觉恢复如何有助于水生场景中的对象检测
Reveal of Domain Effect: How Visual Restoration Contributes to Object Detection in Aquatic Scenes
论文作者
论文摘要
水下机器人感知通常需要视觉恢复和对象检测，这两者已经进行了多年的研究。同时，数据域对现代数据驱动的倾斜过程产生了巨大影响。但是，准确地表明域效应，恢复与检测之间的关系尚不清楚。在本文中，我们通常研究质量多样性数据域与检测性能的关系。同时，我们公布了视觉恢复如何在实际水下场景中有助于对象检测。根据我们的分析，报道了五个关键发现：1）域质量对域内卷积表示和检测准确性具有无知的影响； 2）低质量的结构域导致跨域检测的概括能力更高； 3）在混合域的学习过程中，低质量的领域几乎无法得到充分学习； 4）降解召回效率，恢复无法提高域内检测准确性； 5）通过减少训练数据和现实世界场景之间的域移动，视觉恢复对在野外检测是有益的。最后，作为一个说明性的例子，我们成功地使用水生机器人成功执行了水下对象检测。
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.
