Approximate Supervised Object Distance Estimation on Unmanned Surface Vehicles

TL;DR

This paper presents a cost-effective supervised object detection approach for approximate distance estimation on unmanned surface vehicles, using a specialized model to detect objects and predict their distances from visual data.

Contribution

It introduces a novel, specialized object detection model that estimates object distances on USVs, reducing reliance on costly sensors and calibration.

Findings

Effective detection and distance prediction in maritime environments

Cost reduction compared to traditional sensors

Application in marine assistance systems

Abstract

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.