태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
Quick-View (QV) Inspection is one commonly-used technology. However, it is quite labor-intensive to find defects from a huge number of QV videos. To tackle this problem, we propose a video defect classification task, which is to predict the categories of pipe defects in a short QV video.
출처에 같은 태스크 이름으로 연결된 데이터셋입니다. 동명 태스크가 포함될 수 있으므로, 비교할 때는 평가 결과의 조건도 확인하세요.
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.
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.