计算机工程与应用2024，Vol.60Issue(11)：328-335,8.DOI:10.3778/j.issn.1002-8331.2302-0331
基于体素化的变电站场景三维目标检测
3D Object Detection in Substation Scene Based on Voxelization
摘要
Abstract
Aiming at the problem of low detection accuracy caused by insufficient target feature extraction in substation 3D scene,a voxelization-based 3D object detection model AugSecond for substation scene is proposed,which is designed based on the Second network structure.It introduces a triple attention mechanism in the voxel feature encoding stage,which focuses on multi-dimensional attention to enhance the key information of the target and reduce the interference of irrelevant feature information.It designes asymmetric sparse convolutional networks,uses asymmetric convolution to improve convolutional kernel representation capabilities and fuses multi-scale features to enrich target geometry informa-tion.Meanwhile,the position regression loss is optimized,and CIoU Loss is used to further consider the geometric corre-lation between bounding boxes to speed up the network convergence.Experiments on self-built power scene data sets and public data sets show that compared with the benchmark model,AugSecond model significantly improves recognition accuracy and has real-time reasoning speed,which proves the effectiveness of the proposed model.关键词
体素化/三维目标检测/三重注意力/非对称稀疏卷积Key words
voxelization/3D object detection/triple attention/asymmetric sparse convolution分类
信息技术与安全科学引用本文复制引用
王大伟,胡帆,张娜,杨罡,鲁霁原,张兴忠..基于体素化的变电站场景三维目标检测[J].计算机工程与应用,2024,60(11):328-335,8.基金项目
国网山西省电力公司科技项目(52053020000W). （52053020000W）
