| 基于轻量型YOLOv5的风机桨叶检测与空间定位 |
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| 引用本文： | 白健鹏,王巍,陈雨溪,焦嵩鸣.基于轻量型YOLOv5的风机桨叶检测与空间定位[J].智能系统学报,2022,17(6):1173-1181. |
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| 作者姓名： | 白健鹏 王巍 陈雨溪 焦嵩鸣 |
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| 作者单位： | 华北电力大学 自动化系，河北 保定 071003 |
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| 摘 要： | 应用无人机对风力发电机进行自主巡检时，需对其桨叶叶尖进行精准定位，同时因机载计算板的计算能力有限，常规目标检测算法检测效率低下。为此提出了一种基于轻量型YOLOv5的风机桨叶检测与空间定位方法，首先对YOLOv5目标检测算法进行轻量化改进，将ShuffleNetv2作为特征提取主干网络；然后利用该算法对风机全景图像中的风机轮毂和桨叶进行检测，以得到轮毂和桨叶叶尖的像素坐标；最后利用无人机位姿信息和空间平面的几何关系，对风机桨叶进行精准定位。实验表明，所改进的目标检测算法以1.536×106的参数量在大疆MANIFOLD2-C上的检测速度提升47%，可达29.4 f/s，所设计的定位方法可对风机桨叶叶尖进行精准定位，水平和高度定位误差均为±5 cm，三维整体定位误差为±10 cm。
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| 关 键 词： | 风力发电机 无人机 目标检测 YOLOv5 轻量化 深度学习 桨叶叶尖 精准定位 |
Detection and spatial location of wind turbine blades based on lightweight YOLOv5 |
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| BAI Jianpeng,WANG Wei,CHEN Yuxi,JIAO Songming.Detection and spatial location of wind turbine blades based on lightweight YOLOv5[J].CAAL Transactions on Intelligent Systems,2022,17(6):1173-1181. |
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| Authors: | BAI Jianpeng WANG Wei CHEN Yuxi JIAO Songming |
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| Affiliation: | Department of Automation, North China Electric Power University, Baoding 071003, China |
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| Abstract: | The application of unmanned aerial vehicles (UAVs) for autonomous inspection of wind turbines requires precise positioning of the paddle blade tips, but the detection efficiency of conventional target detection algorithms is low due to the limited computing power of the onboard computer. Therefore, a method of the blade and spatial location detection of wind turbines based on lightweight YOLOv5 is proposed. Initially, the YOLOv5 target detection algorithm is lightly improved using ShuffleNetv2 as the feature extraction backbone network. The algorithm is then used to detect the hub and blades of the turbine in the panoramic image to obtain the pixel coordinates of the hub and blade tips. Finally, the UAV positional information and geometric relationship between the spatial planes are used to accurately locate the wind turbine blades. The tests show that the improved target detection algorithm with 1.536 × 106 parameters on the DJI MANIFOLD2-C improves detection speed by 47%, up to 29.4 f/s. The designed positioning method can accurately locate the tips of wind turbine blades with both horizontal and height positioning errors of ±5 cm and a three-dimensional overall positioning error of ±10 cm. |
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| Keywords: | wind turbine unmanned aerial vehicle object detection YOLOv5 lightweight deep learning blade tip accurate positioning |
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