# Vessel Velocity Estimation and Docking Analysis: A Computer Vision Approach
## 8 Conclusions  
Both results were classified as good by the port authorities of SUAPE. The camera positioning, which allowed for the utilization of two analytics simultaneously, was a key factor in the implementation of the system at the port. The results of the vessel speed estimation demonstrated the effectiveness of the algorithm in accurately measuring the velocity of approaching vessels. The algorithm was able to capture the dynamic changes in velocity, allowing for precise analysis and monitoring of the vessels' movements. The obtained results not only showcased the algorithm's ability to estimate vessel speed but also highlighted its potential applications in optimizing docking procedures and ensuring safe and efficient maritime operations. These findings contribute to advancing computer vision techniques in the maritime industry, offering valuable tools for enhanced vessel management and navigation. As future work in vessel velocity estimation, refining the algorithms used for motion detection and tracking can improve the model. In addition, other developments, such as exploring advanced computer vision techniques and considering integrating other data sources such as radar or GPS complement the visual analysis. In the initial implementation of our vision-based approach, GPS and radar data will be used as evaluation techniques. The utilization of these devices provides a satisfactory approximation of the velocity. However, in further development, once the precision of the velocity model reaches a mature level, it becomes feasible to rely solely on the camera-based system for velocity estimation. This transition is motivated by the lower maintenance cost of the camera system compared to GPS and radar, making it a more cost-effective solution for long-term operation at the pier. Additionally, incorporating machine learning approaches to learn and adapt to different vessel types and environmental conditions can further enhance speed estimation accuracy. The ropes on the bollards' algorithm presented in this study demonstrate computer vision techniques' utilization in analyzing and monitoring vessel docking processes. By employing advanced algorithms, such as the structural similarity index (SSIM), the algorithm identifies the presence of a tied cable on the mooring bit. However, as discussed in Section 7, certain limitations have been identified. These limitations can be addressed through the utilization of alternative metrics, such as Pearson correlation, and the exploration of techniques such as neural Siamese networks, which offer the potential for further improvement. Despite the identified limitations, the algorithm developed for analyzing the ropes on the bollards can play a significant role in initiating the docking process. Its implementation offers promising prospects for integration into real-world maritime operations. By automating the detection of tied cables, the algorithm can enhance situational awareness, improve docking efficiency, and enhance overall safety in port operations. Moreover, the algorithm's adaptability and scalability make it applicable to various docking scenarios and vessel types, further expanding its potential impact. While the current model focuses on detecting tied cables on the mooring bit, future works could expand the algorithm to include the analysis of other components involved in the mooring process, such as fenders or bollards. Another potential direction is to incorporate the evaluation of rope position in the mooring bit. This inclusion can provide a more comprehensive understanding of the docking dynamics. Additionally, investigating the use of advanced image processing techniques to detect and analyze the tension in the mooring lines can contribute to assessing the stability and safety of the moored vessel. Conceptualization, B.J.T.F., A.R.L.C.I. and N.M.d.S.F. ; methodology, J.V.R.d.A., B.J.T.F., A.R.L.C.I. and N.M.d.S.F. ; software, J.V.R.d.A., A.R.L.C.I. and N.M.d.S.F. ; validation, J.V.R.d.A., A.R.L.C.I. and N.M.d.S.F.