Research on apple point cloud semantic segmentation based on deep learning

Point cloud data can accurately and intuitively reflect the spatial relationship between apples and branches.Because of their regularity of point cloud data, traditional convolution neural network is not suitable for point cloud data.Therefore, a semantic segmentation method of apple point cloud based on improved dynamic graph convolution is proposed. Based on Dynamic Graph Convolution Network（DGCNN）, K-Nearest Neighbors（KNN） of different scales are used to construct the neighborhood relationship of each node. Adding neighbor node information in the edge convolution（EdgeConv） is to extract more abundant local features. A graph based attention module is designed to assign different weights to the K nearest neighbor points of the center point. Compared with using maximum pooling to aggregate features, this attention module can better aggregate the feature information of the neighborhood. Channel attention module is introduced to assign different weights to different features. The experimental results show that the network has a higher point cloud segmentation accuracy on the apple point cloud dataset, and the overall accuracy of OA and the average intersection ratio of MIOU reach 91. 2% and 69. 2%, respectively, OA and MIOU are 3. 9% and 3. 6% higher, compared with DGCNN.