摘要
鸟类活动对变电站电力设备的危害由来已久,针对目前探驱鸟设备存在的检测精度低、驱鸟效果差等问题,设计了一种结合视听感知模块、声光驱鸟模块、远端控制模块于一体的智能探驱鸟机器人系统,采用基于粒子群优化的到达时间差(TDOA)算法实现麦克风阵列的声源定位,并采用改进的YOLOv5和DeepSORT算法实现对鸟类目标的精确定位与跟踪,最后基于声光驱鸟设备实现针对性驱鸟。实验结果表明,智能探驱鸟机器人的声源距离估测精度达到96.42%,相位估测误差低于1.1°,视觉识别精度达到89.7%,通过对视听模态数据的融合感知,有效解决了驱鸟设备的视野盲区大幅降低鸟类检测效率的问题,能够更高效准确地完成探驱鸟任务。
Bird activities have threatened the stable operation of electrical equipment in the substations.To address the current equipment's low detection accuracy and poor bird-repelling effect,this article proposes a method that combines an audio-visual perception module,an acoustic-optic bird repelling module and the remote control.The module-in-one intelligent bird detection and repelling robot system uses the time difference of arrival(TDOA)algorithm based on particle swarm optimization to realize the sound source positioning of the microphone array and applies the improved YOLOv5 and DeepSORT algorithms to achieve precise positioning and tracking of bird targets.Finally,the targeted bird repelling is achieved based on acoustic-optic equipment.The experimental results show that the distance estimation accuracy of the sound source reaches 96.42%,the phase estimation error is less than 1.1°,and the visual recognition accuracy reaches 89.7%.The fusion of audio-visual modal data effectively improves bird detection accuracy.The problem of blind spots in the visual field existing in the bird detection process can be solved faster and more accurately to complete the task of detecting and driving birds.
作者
陈通
周鹏
李星宇
汪祝年
娄晨阳
CHEN Tong;ZHOU Peng;LI Xing-yu;WANG Zhu-nian;LOU Chen-yang(State Grid Zhenjiang Power Supply Company,Zhenjiang 212001;School of Software Engineering,Southeast University,Suzhou 215123)
出处
《制造业自动化》
2025年第6期181-188,共8页
Manufacturing Automation
基金
国网江苏省电力有限公司科技项目(J2023029)。
关键词
驱鸟机器人
声源定位
视觉检测
深度学习
视听感知
bird repellent robot
sound localization
visual inspection
deep learning
audio-visual Perception
