【摘要】 水下机器人操作通常需要视觉感知（如目标检测和跟踪），但水下场景视觉质量较差，且代表一种特殊分布，会影响视觉感知的准确性。同时，检测的连续性和稳定性对机器人感知也很重要，但常用的基于静态精度的评估（即平均精度（average precision））不足以反映检测器的时序性能。针对这两个问题，本文提出一种新型机器人视觉感知框架。首先，研究不同质量的数据分布与视觉恢复在检测性能上的关系。结果表明虽然分布质量对分布内检测精度几乎没有影响，但是视觉恢复可以通过缓解分布漂移，从而有益于真实海洋场景的检测。此外，提出基于目标轨迹的检测连续性和稳定性的非参考评估方法，以及一种在线轨迹优化（online tracklet refinement,OTR）来提高检测器的时间性能。最后，结合视觉恢复，建立精确稳定的水下机器人视觉感知框架。为了将视频目标检测（video object detection,VID）方法扩展到单目标跟踪任务，提出小交并比抑制（small-overlap suppression,SOS）方法，实现目标检测和目标跟踪之间的灵活切换。基于ImageNet VID数据集和真实环境下的机器人任务进行了大量实验，实验结果验证了所作分析的正确性及所提方法的优越性。代码公开在https://github.com/yrqs/VisPerception。更多还原

【Abstract】 Underwater robotic operation usually requires visual perception(e.g., object detection and tracking), but underwater scenes have poor visual quality and represent a special domain which can affect the accuracy of visual perception. In addition, detection continuity and stability are important for robotic perception, but the commonly used static accuracy based evaluation(i.e., average precision) is insufficient to reflect detector performance across time. In response to these two problems, we present a design for a novel robotic visual perception framework. First,we generally investigate the relationship between a quality-diverse data domain and visual restoration in detection performance. As a result, although domain quality has an ignorable effect on within-domain detection accuracy,visual restoration is beneficial to detection in real sea scenarios by reducing the domain shift. Moreover, non-reference assessments are proposed for detection continuity and stability based on object tracklets. Further, online tracklet refinement is developed to improve the temporal performance of detectors. Finally, combined with visual restoration,an accurate and stable underwater robotic visual perception framework is established. Small-overlap suppression is proposed to extend video object detection(VID) methods to a single-object tracking task, leading to the flexibility to switch between detection and tracking. Extensive experiments were conducted on the ImageNet VID dataset and real-world robotic tasks to verify the correctness of our analysis and the superiority of our proposed approaches. The codes are available at https://github.com/yrqs/VisPerception.更多还原