• Laser & Optoelectronics Progress
  • Vol. 59, Issue 22, 2210010 (2022)
Wei Gao1, Boyang He1, Ting Zhang2, Meiqing Guo2, Jun Liu2, Huimin Wang2, and Xingzhong Zhang2、*
Author Affiliations
  • 1Internet Department, State Grid Shanxi Electric Power Company, Taiyuan 030021, Shanxi , China
  • 2College of Software, Taiyuan University of Technology, Jinzhong 030600, Shanxi , China
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    DOI: 10.3788/LOP202259.2210010 Cite this Article
    Wei Gao, Boyang He, Ting Zhang, Meiqing Guo, Jun Liu, Huimin Wang, Xingzhong Zhang. Three-Dimensional Object Detection in Substation Operation Scene Based on Attention Mechanism[J]. Laser & Optoelectronics Progress, 2022, 59(22): 2210010 Copy Citation Text show less


    The perception of the spatial distance between operators and dangerous equipment is a basic safety management and control task issue in a substation scene. With the advancement of lidar and three-dimensional (3D) vision theory, 3D point cloud target detection can provide necessary technical assistance for downstream spatial distance measurement tasks. Aiming at the problem of inaccurate target detection caused by factors such as complex background and equipment occlusion in the substation scene, based on the PointNet++ model, an improved attention module is introduced in the local feature extraction stage, and a 3D object detection network PointNet suitable for substation operation scene is proposed. First, the network undergoes a two-level local feature extraction to obtain fine-grained features in each local area, then encodes all local features into feature vectors using a mini-pointnet to obtain global features, and finally passes through the fully connected layer to predict the results. Considering the large gap between the number of front and background points in the cloud data of substation sites, this study calculates the classification loss using focal loss to make the network pay more attention to the feature information of the front points. Experiments on the self-built dataset show that the PowerNet has a mean average precision (mAP) value of 0.735, which is greater than previous models and can be directly applied to downstream security management and control tasks.