Computer Vision over 4G/5G Private Network for Real-Time Draft Assessment to Estimate Vessel Productivity

Draft surveys are widely used to estimate cargo mass during bulk vessel loading and unloading; however, conventional procedures depend on manual draft readings that are episodic, labor-intensive, and sensitive to environmental conditions. Existing camera-based automated approaches rely on draft mark recognition or explicit waterline detection, which remain vulnerable to illumination variability, hull fouling, and wave-induced disturbances.

This paper proposes a computer vision framework deployed at the Port of Santos, Brazil, using fixed quay-side cameras and a private 4G network infrastructure for continuous image transmission. Unlike prior methods, the framework estimates emergent hull height by segmenting vessel hull contours from bow and stern viewpoints using customized YOLOv8 instance-segmentation models, without relying on draft marks or waterline detection. Pixel measurements are converted to metric units using a nearby bollard of known height as a local physical reference.

Field experiments monitor a Panamax bulk carrier over approximately 6.5 days, processing more than 34,000 images from each camera at an average rate of 3.7 images per minute. Both bow and stern segmentation models achieve mAP50-95 mask scores of 0.980 and 0.965, respectively, confirming precise and stable hull boundary delineation. Hull height decreases from 8.27 m to 4.64 m at the bow and from 7.98 m to 3.98 m at the stern over the loading period, with coherent and temporally stable trends across independent viewpoints.

The proposed approach delivers repeatable and continuous hull-height estimates under real operational conditions, including variable lighting, background clutter, and partial occlusions, offering a practical and non-intrusive complement to traditional draft surveys for continuous vessel loading monitoring in modern ports.