南方电网技术2024，Vol.18Issue(2)：89-97,9.DOI:10.13648/j.cnki.issn1674-0629.2024.02.010
改进YOLOv5的变电站反无人机目标检测算法
Anti UAV Target Detection Algorithm for Substation Based on Improved YOLOv5
摘要
Abstract
Aiming at the practical problem that substations are prone to encounter unmanned aerial vehicle(UAV)intrusion,an improved anti UAV target detection method is proposed based on YOLOv5.Firstly,a four scale features fusion structure is proposed by improving the original model structure of YOLOv5 to enhance the detection capability of small-scale objects.Secondly,the C3 module in the original model is introduced into the Transformer encoder to improve the learning ability of small target feature information.Finally,the convolution channel attention module is integrated into the network,focusing on the learning of the target area to improve the representation ability of the model for features.The test results show that the overall recognition rate of the improved model is 90.2%,the average accuracy is 89.5%,and the forward reasoning speed is 160 frames per second.In addition,compared with other existing frontier algorithms,the overall performance of this method is better,and it can better meet the real-time detection requirements of anti UAV in substations.关键词
目标检测/YOLOv5/反无人机/通道注意力机制/Transformer编码器Key words
target detection/YOLOv5/anti UAV/channel attention mechanism/Transformer encoder分类
信息技术与安全科学引用本文复制引用
叶采萍,陈炯,马显龙,胡宗杰..改进YOLOv5的变电站反无人机目标检测算法[J].南方电网技术,2024,18(2):89-97,9.基金项目
云南省基础研究计划资助项目(202001AT070006) （202001AT070006）
中国南方电网有限责任公司科技项目(YNKJXM20220051).Supported by the Basic Research Program of Yunnan Province(202001AT070006) （YNKJXM20220051）
the Science and Technology Program of China Southern Power Grid Co.,Ltd.(YNKJXM2 0220051). （YNKJXM2 0220051）
