Omkar2118/datasets huggingface dataset:
QV-Pipe

The QV-Pipe dataset consists of 9.6k videos, which are collected from real-world urban pipes. The total duration of all videos exceeds 55 hours. Moreover, there are 1 normal class and 16 defect classes. Because the pipe situation is complex and multiple defects often appear at the same time, each video is annotated with multiple labels.

Introducing paper: VideoPipe 2022 Challenge: Real-World Video Understanding for Urban Pipe Inspection
CCTV-Pipe

Our CCTV-Pipe dataset consists of 16 defect categories including structural and functional defects in the pipe. It contains 575 videos with 87 hours, which are collected from real-world urban pipe systems. Different from traditional temporal action localization, our goal in this realistic scenario is to find preferable temporal locations of defects from a untrimmed CCTV video, instead of exact temporal boundaries.

Introducing paper: VideoPipe 2022 Challenge: Real-World Video Understanding for Urban Pipe Inspection