关键词: 行道树靶标喷雾, 行道树在线分割, 实例分割, K近邻, 二维激光雷达(LiDAR)

Abstract:

【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.

Key words: targeted spraying of roadside tree, online segmentation of roadside trees, instance segmentation, K-nearest neighbor, light detection and ranging(LiDAR)