Aerial Draft Surveyor (ADS)

Draft surveying is an essential procedure in determining the displacement and loaded cargo weight of bulk carriers. Currently, the most acceptable method is through manual visual observation by trained draft surveyors. However, this process is subjective, error-prone, and unsafe under poor visibility or during rough sea conditions. This study presents an automated computer vision-powered UAV draft surveying system integrating TensorRT Optimized YOLO11n object detection and YOLO11n-seg image segmentation models deployed on an NVIDIA Jetson Orin Nano. The system performs real-time draft estimation by detecting draft marks, segmenting the waterline, and computing draft values using convergence and line-fitting algorithms. Comparative evaluation with licensed human surveyors on 40 paired readings yielded an MAE of 0.1068 m, RMSE of 0.2740 m, and an R² of 0.948, demonstrating human-comparable accuracy. Agreement analysis indicates high reliability (two-way random effects ICC(2,1) = 0.974) and a small mean bias (system − manual = +0.0628 m, 95% limits of agreement: −0.467 m to +0.592 m). Moreover, a paired t-test (t = 1.469, df = 39) found no statistically significant difference between methods (p ≈ 0.150). The results validate that the proposed UAV-driven computer vision system can perform reliable, real-time draft surveying with accuracy comparable to human experts.