TY - JOUR
T1 - ROV Inspection System with Vision-based Color Correction and Tracking Algorithm for High Depth and Low Light Ecosystems
AU - Arce, Diego
AU - Rodriguez, Laureano
AU - Segovia, Alexander
AU - Vargas, Miguel
AU - Carranza, Cesar
AU - Cuellar, Francisco
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - In this paper we present the design, implementation and preliminary tests of a high depth underwater ROV inspection system. The ROV is equipped with a sensor-based system in order to characterize the water quality of marine ecosystems, and a vision-based system to identify the marine species. The vision system includes an adaptive color correction algorithm and a tracking algorithm that improves in real time the tracking of living species during inspection. A prototype was implemented to validate the functionality of the ROV operation and the vision system algorithms.
AB - In this paper we present the design, implementation and preliminary tests of a high depth underwater ROV inspection system. The ROV is equipped with a sensor-based system in order to characterize the water quality of marine ecosystems, and a vision-based system to identify the marine species. The vision system includes an adaptive color correction algorithm and a tracking algorithm that improves in real time the tracking of living species during inspection. A prototype was implemented to validate the functionality of the ROV operation and the vision system algorithms.
KW - ROV
KW - Tracking algorithm
KW - underwater inspection
UR - http://www.scopus.com/inward/record.url?scp=85131668728&partnerID=8YFLogxK
U2 - 10.1109/OCEANSChennai45887.2022.9775317
DO - 10.1109/OCEANSChennai45887.2022.9775317
M3 - Conference article
AN - SCOPUS:85131668728
SN - 0197-7385
JO - Oceans Conference Record (IEEE)
JF - Oceans Conference Record (IEEE)
T2 - OCEANS 2022 - Chennai
Y2 - 21 February 2022 through 24 February 2022
ER -
