  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.
