为了实现无人船自主导航过程中对障碍物的精确检测，提出了一种基于四叉树扇形层值聚类的无人船障碍物检测方法。首先基于四叉树扇形划分进行障碍物点云数据的检索，并剔除扇形象限内不可信数据；然后利用所获得的四叉树层值来求取全局密度距离，进而获得层值阈值，以此来对不规则多线形障碍物特征进行检测；最后通过建立数据点之间的空间拓扑关系来求取参考距离，并以参考距离为基准对障碍物点云数据进行聚类判定，提高聚类分割准确性。多线形障碍物特征识别性能测试及水面无人船障碍物检测实验结果表明，相较于其他密度聚类算法，在正检率、误检率和漏检率性能指标方面，多线形障碍物特征识别性能测试中，所提算法分别平均下降了9.86%、5.04%、3.10%，水面无人船障碍物检测中，所提算法分别平均下降了10.50%、6.97%、2.95%。

In order to realize the precise detection of obstacles in the process of autonomous navigation of unmanned ships, an obstacle detection method for unmanned ships based on quadtree sector layer value clustering was proposed. Firstly, the obstacle point cloud data was retrieved based on the quadtree sector division, and the untrustworthy data in the sector image limit was eliminated. Secondly, the obtained quadtree layer value was used to calculate the global density distance, and then the layer value threshold was obtained to detect irregular multi-linear obstacle features. Finally, the reference distance was obtained by establishing the spatial topological relationship between data points, and the obstacle point cloud data was clustered and judged based on the reference distance to improve cluster segmentation accuracy. The results of multi-linear obstacle feature recognition performance test and surface unmanned ship obstacle detection experiment show that compared with other density clustering algorithms, in terms of positive detection rate, false detection rate and missed detection performance index, the proposed algorithm decreases by 9.86%, 5.04% and 3.10% respectively during multi-linear obstacle feature recognition performance test, and the proposed algorithm decreases by 10.50%, 6.97% and 2.95% respectively during surface unmanned ship obstacle detection experiment. In the performance indicators of positive detection rate, false detection rate, and missed detection rate.