## 6 Conclusions  
Developing detection methods for vessels and activities around port environments has always been a very challenging task. It is even more difficult when the target vessels are small, and especially when it starts to get dark. Therefore, in this study, we developed an improved multi-structural morphology approach to effectively suppress not only the background noise but also congestion interference at sea so that vessels can be made visible. This makes it possible to accurately detect vessels around surveillance areas in real time. The results of these experiments show that the proposed approach can effectively detect vessels and meet the real-time requirements of video image processing. Additionally, several tests were conducted with four other methods; the results confirm the effectiveness of the proposed approach in terms of its detection accuracy (94%) and processing time (1.23 s). Out of 60 trials, we recorded only a 4% false detection rate, thus proving the reliability of this approach. However, it is worth mentioning that in practice, each method has distinct advantages and their effectiveness depends on the operating environment.  
In practice, there is no absolute ideal filtering method, but the characteristics of the denoising results show the effectiveness of the proposed method in terms of its performance. However, it is worth mentioning that all the experiments in this study were carried out in a single port environment, which was the only one made available to us. We hope to have the opportunity to use our system in another port environment to evaluate the results elsewhere. Additionally, this approach is applicable to surveillance camera systems. We believe that its detection accuracy will be lower for the smallest vessels and smaller activities around port environments. For this reason, the proposed approach should be further improved.  
Therefore, in our future work we will focus on refining the structure of the proposed approach and improving the four-dimensional target feature vector ability for effective tracking. We also plan to combine our proposed approach with deep learning techniques for the identification, recognition, and classification of vessels and activities around port environments. This should increase the overall detection accuracy of this approach.  
Conceptualization, B.M.T.F. and J.A.; methodology, B.M.T.F.; software, J.A.; validation, J.A., H.C.E.-D., and W.Z.; formal analysis, B.M.T.F.; investigation, H.C.E.-D.; resources, W.Z.; data curation, B.M.T.F.; writing--original draft preparation, B.M.T.F.; writing--review and editing, B.M.T.F.; visualization, B.M.T.F. and H.C.E.-D.; supervision, W.Z. and H.C.E.-D.; project administration, W.Z. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The Institutional Review Board of Shanghai Jianqiao University and the University of Ebolowa have approved the study. The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author. The authors would like to say thank you to the R&D Center of Intelligent Systems of Shanghai Jianqiao University and the Electrical, Electronic and Energy Systems Laboratory of the University of Ebolowa for the use of their equipment. Many thanks to the staff of both laboratories. We would also like to say thank you to the all workers at the port (the port of Zhangjiagang City on the East China Sea) where the data were collected for their assistance, kindness, and help (the use of their materials for experiments). The authors declare that they have no known competing financial interests or personal relationships that could appear to influence the work reported in this paper. The authors declare no conflicts of interest.