Multisensor Fusion-Based Maritime Ship Object Detection Method for Autonomous Surface Vehicles.

Autonomous surface vehicles face the challenge of accurately detecting nearby ships in the complex and ever-changing maritime environment, which is vastly different from land areas. To address this issue, we propose an image-based multisensor fusion object detection method that combines Light Detection and Rangings and cameras. Since point clouds have poor semantics, our method primarily relies on images, with point clouds used to support image detection. Our image detection scheme employs a tracking-assisted detection method that leverages historical information to compensate for possible detection failures. Additionally, we designed a confidence-association-based fusion strategy to determine the final targets among the candidates. We conducted field experiments in an open-sea area to demonstrate the accuracy and robustness of our method. The results of these experiments showed that our method is highly accurate and robust in challenging maritime scenarios. Our code and data set will be released on .