# Proposal of Innovative Methods for Computer Vision Techniques in Maritime Sector
## 3 A Novel Application of CV in the Maritime Domain  
In this section, we innovatively put forward a novel application of CV techniques in the maritime domain; this is to enhance the online video viewing experience on ocean-going vessels while saving network resources.  
Network communication services on ocean-going vessels are typically provided by communication satellites, as there are no communication fiber optic cables in the open sea. Consequently, the network speed on ocean-going vessels is relatively slow, and the charges are comparatively high. However, ocean-going vessels often sail on the open sea for weeks or even months, and the crew members on board have a practical demand for Internet access to watch online videos such as news programs and movies, which require fast and cost-effective network communication services. One of the existing solutions is to reduce the network transmission load by compressing and decompressing video files, aiming to save network resources; however, this method is not timely and convenient enough because the compression and decompression processes are time-consuming and may potentially decrease the resolution of online videos. Therefore, the online video viewing experience is not good enough.  
We propose a novel solution that uses a CV model and the concept of transfer learning to achieve a better online video viewing experience while conserving network resources. Transfer learning (2017) is a machine learning method that applies knowledge and modelslearned from old domains to new related ones based on the similarity between problems. The core idea is that the knowledge contained in a model trained on one task can be partially or fully transferred to another similar task.  
Specifically, we pre-train a CV model based on a large amount of video sample data to convert low-resolution videos into high-resolution ones. When crew members on ocean-going vessels watch videos online, a software tool (embedded with the pre-trained CV model), first loads the high-resolution frames of the beginning portion of the video and the low-resolution frames of the remaining portion. For example, it loads the first 10% of the video in high resolution and the remaining 90% in low resolution. The first 10% high-resolution part is used to fine-tune the built-in pre-trained CV model through transfer learning, as the specific features of different types of videos, such as news programs and movies, may vary. The optimized CV model is better able to adapt to the characteristics of the particular video after transfer learning, and then it converts the remaining low-resolution part into a high-resolution one; the converted remainder is then combined with the first 10% part to result in the final video for viewing by the crew members. The entire process, including video loading and CV model fine-tuning, is accomplished automatically in the software. While the resulting video may differ from the original high-resolution video, this method can enhance the online video viewing experience to some extent while conserving network resources. For example, a high-resolution movie requires 2 GB of data usage, but using this method may only require 0.5 GB of data while providing a similar viewing experience. The flowchart of our proposed novel solution utilizing a CV model is shown in Figure 1.