# Automated Docking System for LNG Loading Arm Based on Machine Vision and Multi-Sensor Fusion
## 1 Introduction  
Liquefied natural gas (LNG) is a key clean energy source. Thanks to its efficient volume reduction and convenient storage and transportation characteristics, it is widely used in energy production, transportation, industry, and other fields, and has become an important part of the global energy supply chain [1, 2, 3]. In recent years, driven by the growing demand for clean energy, the LNG market has expanded rapidly. As a critical device connecting LNG carriers and receiving terminals, the demand for LNG loading arms has increased significantly alongside the growth of LNG trade [4, 5]. These systems must not only ensure the stable transfer of LNG under extremely low-temperature conditions but also maintain operational efficiency and safety in complex marine environments [6, 7, 8]. However, the dynamic environment with significant wave and wind disturbances poses substantial challenges to the operation of loading arms, particularly in balancing docking efficiency and stability during the docking process. At present, the docking process of most loading arms still relies heavily on manual operations, which not only suffer from low efficiency but also depend significantly on the experience of operators, failing to meet the demands for efficient and safe operations in modern ports. Moreover, with rising labor costs, the widespread adoption of mechanical automation,artificial intelligence, and Internet of Things (IoT) technologies has become a key driver for industrial intelligent upgrades [9]. Therefore, achieving automation of the loading-arm docking process, particularly enhancing its adaptability in complex environments, has become an urgent research challenge that needs to be addressed. In recent years, significant progress has been made in the automation of LNG loading-arm docking technology. For instance, Reference [10] proposed an improved design scheme for LNG loading arms based on the TRIZ theory, enhancing their applicability and reliability through optimized structural design and functional modeling. In addition, Reference [11] proposed a binocular stereo vision-based method for pipeline interface recognition and pose detection, achieving high-precision flange positioning through depth information extraction and analysis. These studies provide critical theoretical support for improving the docking accuracy and adaptability of LNG loading arms. In practical applications, some ports have preliminarily implemented the automatic docking functionality of LNG loading arms. For instance, according to the report in Reference [12], certain ports have preliminarily achieved the automatic docking of LNG loading arms, providing a practical case for automation research. However, existing technologies still face limitations in adapting to complex working conditions, improving docking accuracy, and ensuring operational stability. Moreover, rising labor costs have made the integration of mechanical automation, artificial intelligence, and the Internet of Things (IoT) a key driving force for industrial intelligent upgrades [13; 14]. In automation technology, machine vision has emerged as a critical enabling technology for LNG loading arms due to its efficient and precise target detection capabilities [15; 16]. By integrating deep learning models, machine vision can achieve accurate recognition and positioning of target flanges in complex environments, providing a solid foundation for the realization of automated docking. Compared to traditional methods, machine vision technology demonstrates significantly improved robustness and adaptability in dynamic scenarios, making it a crucial tool for addressing automation challenges in complex working conditions. Moreover, recent studies have further demonstrated the wide applicability of machine vision technology in automation. [PERSON] et al. [17] proposed a multi-hole positioning and tracking method based on the YOLOv5 deep learning model.