半监督管道视频时间缺陷区间定位
Semi-Supervised Pipe Video Temporal Defect Interval Localization
AI总结：
针对下水道管道CCTV检测中时间缺陷定位标注成本高、现有TAL方法迁移效果差的问题，提出半监督多原型视觉里程计注意力引导方法PipeSPO，提升了缺陷区间定位精度。
摘要：
在下水道管道闭路电视（CCTV）检测中，准确的时间缺陷定位对有效进行缺陷分类、检测、分割和量化至关重要。行业标准通常不要求时间区间标注，尽管其比时间点标注更具信息性，但使用全监督方法时会因标注成本增加而受限；此外，管道检测与时间动作定位（TAL）在场景类型和相机运动模式上的差异，阻碍了点监督TAL方法的有效迁移。因此，本研究引入半监督多原型方法PipeSPO，结合视觉里程计增强注意力引导。该方法通过无监督前置任务利用未标记数据，基于视觉里程计特征捕捉相机姿态，使用弱监督多原型方法。在真实数据集上的实验显示，PipeSPO在IoU阈值0.1-0.7上达到41.89%的平均精度，较现有最先进方法提升8.14%。
英文摘要：
In sewer pipe Closed-Circuit Television (CCTV) inspection, accurate temporal defect localization is essential for effective defect classification, detection, segmentation and quantification. Industry standards typically do not require time-interval annotations, even though they are more informative than time-point annotations for defect localization, resulting in additional annotation costs when fully supervised methods are used. Additionally, differences in scene types and camera motion patterns between pipe inspections and Temporal Action Localization (TAL) hinder the effective transfer of point-supervised TAL methods. Therefore, this study introduces a Semi-supervised multi-Prototype-based method incorporating visual Odometry for enhanced attention guidance (PipeSPO). PipeSPO fully leverages unlabeled data through unsupervised pretext tasks and utilizes time-point annotated data with a weakly supervised multi-prototype-based method, relying on visual odometry features to capture camera pose information. Experiments on real-world datasets demonstrate that PipeSPO achieves 41.89% average precision across Intersection over Union (IoU) thresholds of 0.1-0.7, improving by 8.14% over current state-of-the-art methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
