# Automated Docking System for LNG Loading Arm Based on Machine Vision and Multi-Sensor Fusion
## 3 Design of a Vision-Based Deep Learning Loading-Arm Positioning System
### Image Feature Extraction
Recall indicates the proportion of samples that are actually targeted that are correctly predicted, and it is used to measure the model's missed detection. In addition, the average precision ([EMAIL_ADDRESS]) evaluates the model performance by calculating the weighted average of precision and recall at different confidence threshold values. The higher the value of [EMAIL_ADDRESS], the better the overall performance of the model in the object detection task. The experimental results are shown in Figure 10. The performance of the YOLOv8 model in the rough docking and fine docking stages is as follows:  
* Coarse docking: The YOLOv8 model reaches 0.994 in the rough docking stage of [EMAIL_ADDRESS], and the precision-and-recall curves are close to ideal. This shows that the model can quickly and accurately identify flange targets and generate reliable target frame position information, effectively supporting the initial approach of the unloading arm. * Fine docking: During the fine-alignment stage, the model needs to accurately segment the target flange, and its detection accuracy is more demanding. Despite the increased difficulty of the task, the YOLOv8 model still performed well, with [EMAIL_ADDRESS] reaching 0.988. The model can still extract key features of the flange edges when dealing with complex backgrounds and diverse lighting conditions, ensuring high-precision alignment. Figure 10: (**a**) Precision–recall curve for coarse matching datasets. (**b**) Precision–recall curve for fine matching datasets. In this study, we evaluate the performance of the YOLOv8 model during both the coarse and fine docking stages. The FPS (frame per second) measurements for YOLOv8, which are crucial for real-time processing, are provided in Table 2 below:  
As shown in Table 2, YOLOv8 excels in detection speed during the coarse docking stage, achieving a high FPS of 42. This demonstrates its ability to quickly and efficiently identify flange targets for initial positioning. In the fine docking stage, YOLOv8 focuses on segmentation, with a slightly reduced FPS of 39. Despite the decrease in speed, the model still maintains robust performance, ensuring high-precision segmentation for accurate alignment of the unloading arm. The overall computation FPS, which includes the time for both YOLOv8's task (detection or segmentation) and coordinate calculation, shows a decrease, particularly during fine docking. This reduction reflects the increased computational load during the fine docking phase due to the more complex task of precise alignment and 3D coordinate calculations. From the above analysis, it can be concluded that YOLOv8 is highly effective in both coarse and fine docking stages, meeting the real-time processing requirements for the docking system despite varying computational demands in each stage. Future work will explore improvements to the YOLOv8 network structure to further enhance its performance, particularly with respect to computational efficiency and real-time processing. These improvements could include network pruning, optimization techniques, or the adoption of lightweight versions of the model to achieve faster processing speeds without sacrificing accuracy.