自航船模点云数据集的海上船舶检测

Marine ship detection on the point cloud dataset of autonomous navigation ship models

为了进行激光雷达海上目标检测的算法研究,本文利用自主航行船模与激光雷达等效采集海上场景点云数据,制作了船舶点云数据集。利用深度学习方法,提出了一种适用于船舶点云目标检测的点结构轻量型目标检测网络LASSD,并通过网络剪枝的方式提升了速度并缩减了所需的计算资源。提出一种基于候选目标的高阶点云特征局部注意力模块,弥补网络剪枝带来的精度损失。实验表明:本文的LASSD网络仅使用5.3×106的参数量在船舶数据集中达到79.42%的精度,在检测中单幅场景仅花费13.5 ms,检测精度以及运行速度能够在实际应用中提供实时有效的检测结果。

In order to start the research work of LiDAR maritime object detection,a ship point cloud dataset was established by equivalently collecting the marine point cloud data using autonomous navigation ship models and LIDAR.Using the deep learning method,a point-structured lightweight object detection network LASSD was proposed for ship point cloud object detection,and the network pruning method improved the speed and reduced the required computing resources.A local attention module for high-level point cloud features based on candidate targets was proposed to compensate for the accuracy loss caused by network pruning.Experiments show that the LASSD network in this paper uses only 5.3×106 parameters to achieve 79.42%accuracy in the ship dataset,and single scene detection only takes 13.5 ms.Detection accuracy and operation speed can provide valid results in practical applications.