改进YOLOv5的变电站反无人机目标检测算法OA北大核心CSTPCD
Anti UAV Target Detection Algorithm for Substation Based on Improved YOLOv5
针对目前变电站容易遭遇无人机入侵所面临的实际问题提出了一种改进YOLOv5的反无人机目标检测方法.首先,通过改进YOLOv5原模型结构提出四尺度特征融合结构,增强小尺度物体的检测能力;其次,将原有模型内C3模块引入Transformer编码器,提升小目标特征信息学习能力;最后,将卷积通道注意力模块集成到网络中,专注于目标区域的学习以提升模型对于特征的表征能力.测试结果表明,改进后模型整体识别率达到90.2%,平均精度可达89.5%,前向推理速度可达160帧/s.此外,综合对比现有其他前沿算法,该方法整体性能更优,更能满足变电站反无人机的实时检测需求.
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.
叶采萍;陈炯;马显龙;胡宗杰
上海电力大学电气工程学院,上海 200090云南电网有限责任公司电力科学研究院,昆明 650217
计算机与自动化
目标检测YOLOv5反无人机通道注意力机制Transformer编码器
target detectionYOLOv5anti UAVchannel attention mechanismTransformer encoder
《南方电网技术》 2024 (002)
89-97 / 9
云南省基础研究计划资助项目(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);the Science and Technology Program of China Southern Power Grid Co.,Ltd.(YNKJXM2 0220051).
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