# Proposal of Innovative Methods for Computer Vision Techniques in Maritime Sector
## 2 Innovative Methods to Further Improve the Recognition Accuracy of CV Models
### Enhancing Recognition Accuracy through Ensemble Learning  
Illegal shipping activities not only increase the occurrence of maritime traffic accidents and impact regular maritime transportation but also potentially harm the marine ecological environment, through activities such as illegal river vessels entering the sea, illegal fishing, and the illegal excavation/dumping of sand. To enhance the surveillance of maritime vessels, cameras are installed for surveillance in key areas such as river estuaries and sea entrances, where some illegal shipping activities may happen. Then, CV models are utilized to recognize the specific type of ships in the captured images of these cameras. For example, when a river vessel is identified as preparing to sail into the open sea, the recognition system will trigger an alarm and alert surveillance officers to take enforcement actions. However, due to the complex conditions and environments, such as heavy fog and water surface light reflection, the accuracy of ship recognition by the CV model is relatively low; for instance, mistakenly identifying a sea vessel as a river vessel is possible, resulting in inefficient management.  
Existing research has primarily focused on optimizing the accuracy and precision of image recognition by improving the CV model itself [23]. However, the improvement in accuracy is limited due to the complicated environment. Ensemble learning [25] is a machine learning method that completes learning tasks by constructing and combining multiple learners. The core idea is to integrate multiple learners (weak learners) to achieve better performance than a single learner. By combining the prediction results of multiple learners, ensemble learning can improve overall generalization ability and stability, thereby achieving better performance in complex tasks. In other words, even if one weak learner obtains an incorrect prediction, other weak learners may correct the error. Based on the concept of ensemble learning, we propose an innovative approach to addressing the issue by installing multiple cameras at different orientations in key areas to capture images of ships from various angles. Specifically, the captured images from each orientation are independently processed using a CV model for recognition, and the majority (more than half) of recognition results are taken as the final recognition output. Therefore, even if the recognition result of the image captured from a certain angle is incorrect, as long as the majority of the recognition results are accurate, the output result is correct. The proposed method, thereby, can enhance the accuracy of detection and recognition, with more details illustrated in Example 1.  
**Example 1**.: _At an estuary, nine cameras are installed at different orientations to identify illegal entries of river ships into the sea. Assume each camera has a recognition accuracy rate of 70%, and the recognition results from photos taken at different angles are independent of each other. Suppose at a given moment, the system captures nine photos from different angles, and as long as five or more (more than half) photos are correctly recognized, the system's output is correct, with the accuracy rate \(R\) calculated as follows:_\[R=\sum_{k=5}^{9}C_{b}^{k}0.7^{k}(1-0.7)^{9-k}\approx 90.12\%,\]  
_where \(C_{b}^{k}\) represents the number of combinations of \(k\) elements taken from nine different elements. The accuracy rate of the system with nine cameras increases by 28.74% compared to the recognition accuracy rate of 70% of a single camera._