Point cloud projection combined with lightweight convolutional neural network realizes fast target classification of 3D imaging sonar
The imaging result of the three-dimensional(3D)imaging sonar is a point cloud,and the network of point cloud target recognition is characterized by complex network structure and large computation.We propose a method to project the imaging result of three-dimensional imaging sonar from a point cloud to an image,and use lightweight convolutional neural networks to achieve fast target classification for three-dimensional imaging sonar.Firstly,the method performs maximum filtering and threshold filtering on the beam domain data after the beamforming of the 3D imaging sonar beam to reduce the dimensionality of the point cloud.Next,based on the beam direction of the 3D imaging sonar,the point cloud is projected to a depth image and an intensity image to save the point cloud position information and intensity information respectively.Then,the mixed image is constructed using the depth image and the intensity image as the first channel and the second channel,and the mixed image is used as the input of the target classification network,thus converting the target classification problem of 3D point clouds into the target classification problem of images.Finally,3D imaging sonar fast target classification was implemented using MobileNetV2.The experimental results show that the projection method proposed in this paper can be used to complete the target classification task of three-dimensional imaging sonar point cloud by an image classification network.Moreover,the convergence rate of the mixed channel image is significantly faster than that of the separate intensity image and depth image,and the target classification can be conducted in real time with the combination of the target recognition network,achieving an accuracy of 91.13％on the real data set.
NETL
NSTL
万方数据
维普
