A Real-Time Draft Remote Measurement Framework using Unmanned Aerial Vehicles

Measuring a ship's draft is a critical task that enhances the efficiency of bulk cargo transportation and prevents accidents such as vessel damage due to exceeding maximum draft depth. Traditional draft measurement methods require workers to approach the exterior of the ship for measurement, posing risks of accidents caused by marine environmental factors like waves and wind. To address these issues, this paper proposes an automatic draft measurement framework utilizing drones. The proposed framework estimates the drone's position to transform the images into undistorted frontal views of the draft marks and accurately detects the waterline without the need for training by employing a segmentation foundation model. Subsequently, a YOLO model is used to detect the draft marks, and the final draft depth is measured based on the detected waterline and draft marks. Experimental results demonstrate that the proposed framework achieves a rapid execution time of 0.853 seconds, including both drone pose estimation and draft measurement stages. This is significantly shorter than the time required for manual measurement by workers, indicating its potential for real-time application. Moreover, an average draft measurement error of less than 20mm showcases a high level of accuracy suitable for practical field implementation.