Authors:
Rogerio Hart
and
Aura Conci
Affiliation:
Institute of Computing, Universidade Federal Fluminense, Niteroi, Rio de Janeiro, Brazil
Keyword(s):
Video Analysis, Segmentation, Flow Rate, Neural Network Model, Offshore Substructure.
Abstract:
This work presents two approaches for detecting and quantifying the offshore flow of leaks, using video recorded by a remote-operated vehicle (ROV) through underwater image analysis and considering the premise of no bubble overlap. One is designed using only traditional digital image approaches, such as Mathematical Morphology operators and Canny edge detection, and the second uses segmentation Convolutional Neural Network. Implementation and experimentation details are presented, enabling comparison and reproduction. The results are compared with videos acquired under controlled conditions and in an operational situation, as well as with all previous possible works. Comparison considers the estimation of the average diameter of rising bubbles, velocity of rise, leak flow rate, computational automation, and flexibility in bubble recognition. The results of both techniques are almost the same depending on the video content in the analysis.