  Today ship hull inspection including the examination of the external coating,
detection of defects, and other types of external degradation such as corrosion
and marine growth is conducted underwater by means of Remotely Operated
Vehicles (ROVs). The inspection process consists of a manual video analysis
which is a time-consuming and labor-intensive process. To address this, we
propose an automatic video analysis system using deep learning and computer
vision to improve upon existing methods that only consider spatial information
on individual frames in underwater ship hull video inspection. By exploring the
benefits of adding temporal information and analyzing frame-based classifiers,
we propose a multi-label video classification model that exploits the
self-attention mechanism of transformers to capture spatiotemporal attention in
consecutive video frames. Our proposed method has demonstrated promising
results and can serve as a benchmark for future research and development in
underwater video inspection applications.
