Context-based and image-based subsea pipeline degradation monitoring

Abstract This research examines the factors contributing to exterior material degradation of subsea oil and gas pipelines monitored with autonomous underwater systems (AUS). The AUS have a role gathering image data that is further analyzed artificial intelligence analysis methods. Corrosion potential ruptures on pipeline surfaces are complex processes involving several competing elements, such as geographical properties, composition soil, atmosphere, marine life, whose eflt in substantial environmental damage financial loss. Despite extensive research, corrosion monitoring prediction remain persistent challenge industry. There lack knowledge map can enable ausing an recognize ongoing potentially prevent damage. main contribution this for increased context risk awareness improve reliability image-based inspection by detecting hazards early signs surfaces.