Approximate Supervised Object Distance Estimation on Unmanned Surface Vehicles
This work addresses distance estimation for USV operators, but it is incremental as it adapts existing object detection methods to a specific domain.
The paper tackles the problem of costly and complex distance estimation for unmanned surface vehicles by introducing a supervised object detection approach that predicts object distances from images, offering a cost-efficient alternative with application in marine assistance systems.
Unmanned surface vehicles (USVs) and boats are increasingly important in maritime operations, yet their deployment is limited due to costly sensors and complexity. LiDAR, radar, and depth cameras are either costly, yield sparse point clouds or are noisy, and require extensive calibration. Here, we introduce a novel approach for approximate distance estimation in USVs using supervised object detection. We collected a dataset comprising images with manually annotated bounding boxes and corresponding distance measurements. Leveraging this data, we propose a specialized branch of an object detection model, not only to detect objects but also to predict their distances from the USV. This method offers a cost-efficient and intuitive alternative to conventional distance measurement techniques, aligning more closely with human estimation capabilities. We demonstrate its application in a marine assistance system that alerts operators to nearby objects such as boats, buoys, or other waterborne hazards.