# Proposal of Innovative Methods for Computer Vision Techniques in Maritime Sector
## 1 Introduction  
With the popularity and development of artificial intelligence (AI), big data, and the Internet of Things (IoT), computer vision (CV) techniques have been extensively utilized in the shipping industry and maritime research, including maritime surveillance [1], maritime safety [2], maritime transportation [3], marine environmental protection [4], and international maritime policies and laws [5]. Table 1 shows the SWOT analysis for the applications of CV techniques in the maritime domain. CV techniques present distinct advantages in the shipping industry, facilitating the real-time monitoring and automatic detection of vessels, cargo, and marine environments. This capability not only reduces operational costs but also enhances the efficiency and safety of maritime operations. With an increasing global demand for automation and intelligence in the shipping industry, the application and proliferation of CV techniques are facing unprecedented opportunities. Governments in many countries and regions are also actively supporting the development of smart shipping and maritime safety technologies. However, the accuracy and stability of CV models are critically dependent on the availability of extensive and high-quality training data in the maritime domain and may be influenced by complex maritime environments. On the other hand, the application of CV techniques may encounter legal requirements concerning data privacy and security, which could pose challenges. Furthermore, their widespread adoption may also raise social and ethical issues such as employment impacts on the traditional shipping industry. Consequently, the widespread implementation of CV techniques still encounters numerous challenges and issues. One of the most important issues of CV techniques in the shipping industry is the timely, automatic, and accurate detection and recognition of water surface targets. Many traditional CV models and algorithms have been employed to detect and recognize ships and objects of interest, including mean shift [6], deformable part-based models (DPMs) [7], support vector machines (SVMs) [8], and sparse representation [9]. [PERSON] et al. [10] provide a comprehensive overview of various CV models for target detection and tracking in the maritime environment, and the performance of several maritime and computer vision techniques is evaluated on the Singapore Maritime Dataset (SMD), a dataset including on-shore videos, on-board videos, and near-infrared (NIR) videos, which are acquired at various locations. [PERSON] et al. [11] introduce an innovative approach that merges CV and trajectory classification to provide a highly accurate classification of vessel mobility patterns. [PERSON] et al. [12] present a large-scale water surface object detection and tracking benchmark, which aims to bridge the gap between land-based vision systems and sea-based ones. [PERSON] et al. [13] discuss and define the assessment metrics suitable for maritime CV, indicating that existing CV approaches are indeed promising for maritime research. In the past several years, maritime research has witnessed and benefited from the applications of deep learning methods with superior performance in target detection and recognition. [PERSON] et al. [14] make a survey on four aspects of current research progress: full scene parsing of an image, target vessel re-identification, target vessel tracking, and multimodal data fusion with data from visual sensors. Deep learning methods, such as regions with convolutional neural network features (R-CNN) [15], Fast R-CNN [16], and Faster R-CNN [17], are proposed and adopted, which can obtain accurate detection results at the expense of a high computational cost. For a balance between detection accuracy and computational efficiency, the method called \"you only look once\" (YOLO) [18], which is based on image global information, is widely employed in practical applications. [PERSON] et al. [2] develop an accurate ship fire detection model based on YOLO for discerning the presence of fires on vessels.