Abstract
Segmentation of plant point clouds to obtain high-precise morphological traits is essential for plant phenotyping and crop breeding. Although the bloom of deep learning methods has boosted much research on the segmentation of plant point cloud, most works follow the common practice of hard voxelization-based or down-sampling-based methods. They are limited to segmenting simple plant organs, overlooking the difficulties of resolving complex plant point clouds with high spatial resolution. In this study, we propose a deep learning network plant segmentation transformer (PST) to realize the semantic and instance segmentation of MLS (Mobile Laser Scanning) oilseed rape point cloud, which characterizes tiny siliques and dense points as the main traits targeted. PST is composed of: (i) a dynamic voxel feature encoder (DVFE) to aggregate per point features with raw spatial resolution; (ii) dual window sets attention block to capture the contextual information; (iii) a dense feature propagation module to obtain the final dense point feature map. The results proved that PST and PST-PointGroup (PG) achieved state-of-the-art performance in semantic and instance segmentation tasks. For semantic segmentation, PST reached 93.96%, 97.29%, 96.52%, 96.88%, and 97.07% in mean IoU, mean Precision, mean Recall, mean F1-score, and overall accuracy, respectively. For instance segmentation, PST-PG reached 89.51%, 89.85%, 88.83% and 82.53% in mCov, mWCov, mPerc90, and mRec90, respectively. This study extends the phenotyping of oilseed rape in an end-to-end way and proves that the deep learning method has a great potential for understanding dense plant point clouds with complex morphological traits.
Abstract (translated)
URL
https://arxiv.org/abs/2206.13082
