To address the problems of disorder,uneven sampling,and the poor segmentation of workpiece point clouds with mutual occlusion,a multiscale adaptive channel attention point cloud segmentation network(PointECA)was proposed.In this algorithm,multi-scale feature extraction module was used to better fuse the local neighborhood features of different scales and richer global feature infor-mation was obtained;the adaptive channel attention module was used to interactively learn the channel dimensions of local features at different scales to achieve a better semantic segmentation effect.In addition,the Workpieces dataset for semantic segmentation experi-ments was produced.A large amount of experimental data shows that PointECA achieves 95.42％mean intersection over union for work-piece part segmentation in disordered and mutually occluded scenes,which can provide better conditions for the fast sorting disordered workpieces.
维普
万方数据