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 https://github.com/flakeice/mssd.

中文翻译：

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基于多传感器融合的自主水面车辆海上船舶目标检测方法

自主水面车辆面临着在复杂且不断变化的海洋环境中准确检测附近船只的挑战，这与陆地区域有很大不同。为了解决这个问题，我们提出了一种基于图像的多传感器融合目标检测方法，该方法结合了光检测和测距以及相机。由于点云的语义较差，我们的方法主要依赖于图像，用点云来支持图像检测。我们的图像检测方案采用跟踪辅助检测方法，利用历史信息来补偿可能的检测失败。此外，我们设计了一种基于置信关联的融合策略来确定候选者中的最终目标。我们在公海地区进行了现场实验，以证明我们方法的准确性和鲁棒性。这些实验的结果表明，我们的方法在具有挑战性的海上场景中具有高度准确性和鲁棒性。我们的代码和数据集将在 https://github.com/flakeice/mssd 上发布。