Version 1
: Received: 24 September 2024 / Approved: 24 September 2024 / Online: 24 September 2024 (14:09:10 CEST)
How to cite:
Tabi Fouda, B. M.; Zhang, W.; Atangana, J.; Edima-Durand, H. C. Research on a Key Method for the Optimization of Port Vessel Detection Based on an Improved Multi-Structural Morphology Approach. Preprints2024, 2024091935. https://doi.org/10.20944/preprints202409.1935.v1
Tabi Fouda, B. M.; Zhang, W.; Atangana, J.; Edima-Durand, H. C. Research on a Key Method for the Optimization of Port Vessel Detection Based on an Improved Multi-Structural Morphology Approach. Preprints 2024, 2024091935. https://doi.org/10.20944/preprints202409.1935.v1
Tabi Fouda, B. M.; Zhang, W.; Atangana, J.; Edima-Durand, H. C. Research on a Key Method for the Optimization of Port Vessel Detection Based on an Improved Multi-Structural Morphology Approach. Preprints2024, 2024091935. https://doi.org/10.20944/preprints202409.1935.v1
APA Style
Tabi Fouda, B. M., Zhang, W., Atangana, J., & Edima-Durand, H. C. (2024). Research on a Key Method for the Optimization of Port Vessel Detection Based on an Improved Multi-Structural Morphology Approach. Preprints. https://doi.org/10.20944/preprints202409.1935.v1
Chicago/Turabian Style
Tabi Fouda, B. M., Jacques Atangana and Hélène Carole Edima-Durand. 2024 "Research on a Key Method for the Optimization of Port Vessel Detection Based on an Improved Multi-Structural Morphology Approach" Preprints. https://doi.org/10.20944/preprints202409.1935.v1
Abstract
Some vessels do not comply with maritime rules, particularly in port and safety zones; they must be detected to avoid incidents. Due to background noise and congestion interference at the sea surface, it is difficult to accurately detect these vessels when it is getting dark, especially smaller ones. For these reasons, this paper proposes and develops an improved multi-structural mor-phology (IMSM) approach to eliminate all this noise and interference. The vessel target is sepa-rated from the sea surface backdrop using weighted morphological filtering of many groups of structural components. Then, the neighborhood-based adaptive fast median filtering is used to filter out impulse noise. Finally, the characteristic morphological model of the vessel target is established using the connected domain; this permits eliminating the sea surface congestion and locating vessels movement in real-time. Multiple tests were carried out in a small and discon-tinuous area of the vessel’s inter-frame motion. The results of many groups of data collected show that the proposed approach can effectively eliminate the background noise and congestion in-terference in the monitoring video. The detection accuracy rate and the processing time have increased by approximately 3.91% and 1.14s, respectively.
Keywords
Port vessel detection; weighted morphological filtering; adaptive fast median filtering; connected domain computing; multi-structure morphology; sea surface congestion
Subject
Engineering, Marine Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
