3D Object Detection in Substation Scene Based on Voxelization

被引：0
|
作者：
Wang, Dawei [1 ]
Hu, Fan [1 ]
Zhang, Na [1 ]
Yang, Gang [1 ]
Lu, Jiyuan [2 ]
Zhang, Xingzhong [2 ]
机构：
[1] Electric Power Research Institute, State Grid Shanxi Electric Power Company, Taiyuan,030002, China
[2] Shanxi Hongshuntong Technology Co., Ltd., Taiyuan,030024, China
来源：
Computer Engineering and Applications | 2024年 / 60卷 / 11期
关键词：
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 information. Meanwhile; the position regression loss is optimized; and CIoU Loss is used to further consider the geometric correlation 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. © 2024 Journal of Computer Engineering and Applications Beijing Co; Ltd; Science Press. All rights reserved;
D O I：
10.3778/j.issn.1002-8331.2302-0331
中图分类号：
学科分类号：
摘要：
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页码：328 / 335
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