Real-time three-dimensional ship detection using a monocular camera

Lim, Yeeun

doi:10.1177/00202940251355043

Real-time three-dimensional ship detection using a monocular camera

2026-05-01

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AI

DOI：10.1177/00202940251355043

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In this study, we propose a monocular camera-based 3D detection technique that determines ship attributes, such as position, size, orientation, and relative distance, precisely in real time using visual sensor information. The detection algorithm leverages a keypoint detection network to predict the ship's position, size, and heading direction. To enhance relative distance estimation accuracy, the network incorporates an explicit pixel distance estimation from the horizon line to the target ship. This design allows the network to more effectively exploit geometric cues and thereby enhance distance estimation performance. To acquire training data, AIS data and monocular camera images were acquired synchronously in the vicinity of Busan Port. The AIS data was utilized to define ground truth annotations and label the training dataset. The experimental results confirm that the proposed method showed a 5.4 percentage point improvement in 3D bounding box mAP and 5.79 percentage point enhancement in direction mAP to the baseline model. A novel contribution of this study lies in leveraging the pixel distance from the horizon line as a key geometric cue for improving monocular distance estimation. Furthermore, the integration of synchronously collected AIS data with visual imagery to generate high-quality training labels is a distinctive aspect of our approach.

Keyword:

3D ship detection

monocular camera

automatic identification system

keypoint detection

pixel distance

Real-time three-dimensional ship detection using a monocular camera

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