【目的】针对行道树对靶施药技术中的靶标实时在线分割需求,研究基于移动激光扫描(mobile laser scanning,MLS)的行道树靶标点云在线实时分割方法,建立能够实时在线准确分割行道树靶标点云的点云实例分割算法。【方法】本研究以300 m长街道一侧的行道树为研究对象,通过建立FIFO(first input first output)缓冲区,每隔一段时间读取MLS采集到的三维街道点云数据中的若干帧街道点云数据。将读取过后FIFO缓冲区中的街道点云数据转换为三通道街道图像,使用图像实例分割模型对街道图像进行分割,得到行道树候选实例。然后,对行道树候选实例与已检测到的行道树实例进行实例融合,对已检测到的行道树实例进行完整性检测,对检测完整的行道树实例执行图像-点云映射,得到行道树点云实例。最后,使用阈值滤波与K最近邻(K-nearest neighbor,KNN)两种方法在点云层面对行道树点云实例进行优化。【结果】在阈值滤波参数设置为0.65 m、KNN的半径参数设置为0.5 m时,行道树靶标点云实例分割结果最优,准确率为0.986 5,召回率为0.940 7,F₁分数为0.957 6,平均每帧分割时间为5.261 ms。【结论】本研究提出的行道树靶标点云在线分割方法有效,可以满足行道树靶标实时在线分割的要求。

【Objective】Aiming at meeting the needs of real-time online segmentation of target in the target application technology of street trees, an online real-time segmentation method of street tree target point cloud based on mobile laser scanning (MLS) was studied, and a point cloud instance segmentation algorithm that can accurately segment the target point cloud of street trees in real time and online was established.【Method】A street tree on one side of a 300 m long street was used as the research object. By establishing a FIFO (first input, first output) buffer, several frames of three-dimensional street point cloud data collected by MLS were read at regular intervals. The street point cloud data in the FIFO buffer after reading was converted into a three-channel street image, and the street image was segmented by image instance segmentation model to obtain street tree candidate instances. Subsequently, the street tree candidate instances were fused with the detected street tree instances, the integrity of the detected street tree instances was determined, and image-point cloud mapping was performed on the detected complete street tree instances to obtain the street tree point cloud instances. Finally, threshold filtering and K-nearest neighbor (KNN) were used to optimize the point cloud instances of street trees facing point clouds.【Result】When the threshold filter parameter was set to 0.65 m and the radius parameter of KNN was set to 0.5 m, the segmentation results of the street tree target point cloud instance were optimal, with an accuracy rate of 0.986 5, a recall rate of 0.940 7, an F₁ score of 0.957 6, and an average segmentation time of each frame of 5.261 ms.【Conclusion】The online segmentation method of street tree target cloud proposed in this study is effective and meets the requirements of real-time online segmentation of street tree targets.