TY - JOUR
T1 - Vessel Velocity Estimation and Docking Analysis
T2 - A Computer Vision Approach
AU - de Andrade, João V.R.
AU - Fernandes, Bruno J.T.
AU - Izídio, André R.L.C.
AU - da Silva Filho, Nilson M.
AU - Cruz, Francisco
N1 - Publisher Copyright:
© 2023 by the authors.
PY - 2023/7
Y1 - 2023/7
N2 - The opportunities for leveraging technology to enhance the efficiency of vessel port activities are vast. Applying video analytics to model and optimize certain processes offers a remarkable way to improve overall operations. Within the realm of vessel port activities, two crucial processes are vessel approximation and the docking process. This work specifically focuses on developing a vessel velocity estimation model and a docking mooring analytical system using a computer vision approach. The study introduces algorithms for speed estimation and mooring bitt detection, leveraging techniques such as the Structural Similarity Index (SSIM) for precise image comparison. The obtained results highlight the effectiveness of the proposed algorithms, demonstrating satisfactory speed estimation capabilities and successful identification of tied cables on the mooring bitts. These advancements pave the way for enhanced safety and efficiency in vessel docking procedures. However, further research and improvements are necessary to address challenges related to occlusions and illumination variations and explore additional techniques to enhance the models’ performance and applicability in real-world scenarios.
AB - The opportunities for leveraging technology to enhance the efficiency of vessel port activities are vast. Applying video analytics to model and optimize certain processes offers a remarkable way to improve overall operations. Within the realm of vessel port activities, two crucial processes are vessel approximation and the docking process. This work specifically focuses on developing a vessel velocity estimation model and a docking mooring analytical system using a computer vision approach. The study introduces algorithms for speed estimation and mooring bitt detection, leveraging techniques such as the Structural Similarity Index (SSIM) for precise image comparison. The obtained results highlight the effectiveness of the proposed algorithms, demonstrating satisfactory speed estimation capabilities and successful identification of tied cables on the mooring bitts. These advancements pave the way for enhanced safety and efficiency in vessel docking procedures. However, further research and improvements are necessary to address challenges related to occlusions and illumination variations and explore additional techniques to enhance the models’ performance and applicability in real-world scenarios.
KW - Structural Similarity Index (SSIM)
KW - computer vision
KW - docking analysis
KW - vessel velocity estimation
UR - https://www.scopus.com/pages/publications/85166030752
U2 - 10.3390/a16070326
DO - 10.3390/a16070326
M3 - Article
AN - SCOPUS:85166030752
SN - 1999-4893
VL - 16
JO - Algorithms
JF - Algorithms
IS - 7
M1 - 326
ER -