Fast modeling method of 3D substation model based on improved SSD neural networks and point cloud registration algorithm
In order to improve the efficiency of substation 3D modeling,a rapid modeling method of substation 3D model combining improved SSD(single shot detection)target detection algorithm and ICP(iterative closest point)point cloud registration algorithm is proposed.Aiming at the low accuracy of equipment recognition caused by insufficient number of point cloud training samples,the method quickly transforms 3D model modeling into equipment type,model recognition and point cloud import.First,the improved SSD target detection algorithm is used to initially identify the type of substation equipment,and then the ICP registration algorithm is used to identify the model of substation equipment.The combination of the two methods realizes the accurate identification of the point cloud of substation equipment.Finally,according to the actual pose of the registered equipment in the substation point cloud scene,the registered standard model in the model library is imported into the substation 3D point cloud scene,which greatly improves the modeling efficiency of the substation 3D model.The method has been applied to a substation 3D modeling.The results show that the model identification accuracy of different substation equipment is high,the average modeling time of equipment is 32 s,and the modeling efficiency is much higher than that of manual modeling.
NETL
NSTL
万方数据
