[1]BAI Jianpeng,WANG Wei,CHEN Yuxi,et al.Detection and spatial location of wind turbine blades based on lightweight YOLOv5[J].CAAI Transactions on Intelligent Systems,2022,17(6):1173-1181.[doi:10.11992/tis.202204016]
Copy

Detection and spatial location of wind turbine blades based on lightweight YOLOv5

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 17 Number of periods: 2022 6 Page number: 1173-1181 Column: 学术论文—智能系统 Public date: 2022-11-05
Title:
Detection and spatial location of wind turbine blades based on lightweight YOLOv5
Author(s):
BAI Jianpeng;  WANG Wei;  CHEN Yuxi;  JIAO Songming
Department of Automation, North China Electric Power University, Baoding 071003, China
Keywords:
wind turbine; unmanned aerial vehicle; object detection; YOLOv5; lightweight; deep learning; blade tip; accurate positioning
CLC:
TP138;TP242
DOI:
10.11992/tis.202204016
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.
References:
[1] 陈诗一, 许璐. “双碳”目标下全球绿色价值链发展的路径研究[J]. 北京大学学报(哲学社会科学版), 2022, 59(2): 5–12
CHEN Shiyi, XU Lu. A study on the development path of global green value chain to achieve the targets of carbon peaking and carbon neutrality[J]. Journal of Peking University (philosophy and social sciences edition), 2022, 59(2): 5–12
[2] 蒋元锐. 武钢委员: “双碳”目标呼唤风电赋能[N]. 中华工商时报, 2022-03-11(2).
JIANG Yuanrui. Member WU Gang: The targets of carbon peaking and carbon neutrality call for wind power energization[N]. China bussiness times, 2022-03-11(2).
[3] CHATTERJEE J, DETHLEFS N. Scientometric review of artificial intelligence for operations & maintenance of wind turbines: the past, present and future[J]. Renewable and sustainable energy reviews, 2021, 144: 111051.
[4] 田枫, 白欣宇, 刘芳, 等. 一种轻量化油田危险区域入侵检测算法[J]. 智能系统学报, 2022, 17(3): 634–642
TIAN Feng, BAI Xinyu, LIU Fang, et al. A lightweight intrusion detection algorithm for hazardous areas in oilfields[J]. CAAI transactions on intelligent systems, 2022, 17(3): 634–642
[5] ZHOU Yan, CHEN Shaochang, WANG Yiming, et al. Review of research on lightweight convolutional neural networks[C]//2020 IEEE 5th Information Technology and Mechatronics Engineering Conference. Chongqing: IEEE, 2020: 1713-1720.
[6] 何锐波, 狄岚, 梁久祯. 一种改进的深度学习的道路交通标识识别算法[J]. 智能系统学报, 2020, 15(6): 1121–1130
HE Ruibo, DI Lan, LIANG Jiuzhen. An improved deep learning algorithm for road traffic identification[J]. CAAI transactions on intelligent systems, 2020, 15(6): 1121–1130
[7] JACOB B, KLIGYS S, CHEN Bo, et al. Quantization and training of neural networks for efficient integer-arithmetic-only inference[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City: IEEE, 2018: 2704-2713.
[8] KRISHNAMOORTHI R. Quantizing deep convolutional networks for efficient inference: a whitepaper[EB/OL]. (2018-06-21)[2022-04-11].https://arxiv.org/abs/1806.08342.
[9] LUO JIAN-HAO, WU JIANXIN. An entropy-based pruning method for CNN compression[EB/OL]. (2017-06-19)[2022-04-11].https://arxiv.org/abs/1706.05791.
[10] HINTON G, VINYALS O, DEAN J. Distilling the knowledge in a neural network[EB/OL]. (2015-03-09)[2022-04-11].https://arxiv.org/abs/1503.02531.
[11] LI Yahui, LIU Jun, WANG Lilin. Lightweight network research based on deep learning: a review[C]//2018 37th Chinese Control Conference (CCC). Wuhan: IEEE, 2018: 9021-9026.
[12] SANDLER M, HOWARD A, ZHU Menglong, et al. MobileNetV2: inverted residuals and linear bottlenecks[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City: IEEE, 2018: 4510-4520.
[13] MA Ningning, ZHANG Xiangyu, ZHENG Haitao, et al. ShuffleNet V2: practical guidelines for efficient CNN architecture design[C]//European Conference on Computer Vision. Cham: Springer, 2018: 122-138.
[14] WU Bichen, WAN A, YUE Xiangyu, et al. Shift: a zero FLOP, zero parameter alternative to spatial convolutions[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City: IEEE, 2018: 9127-9135.
[15] HAN Kai, WANG Yunhe, TIAN Qi, et al. GhostNet: more features from cheap operations[C]//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Seattle: IEEE, 2020: 1577-1586.
[16] 潘佳捷. 风机叶片的无人机自主巡检系统[D]. 成都: 电子科技大学, 2020.
PAN Jiajie. An autonomous inspection system for wind turbine blade based on unmanned aerial vehicle[D]. Chengdu: University of Electronic Science and Technology of China, 2020.
[17] 朱凯华. 面向风机叶片巡检的无人机自动航迹线规划研究[D]. 北京: 北京交通大学, 2021.
ZHU Kaihua. Research on automatic path planning of UAV for wind turbine blade inspection[D]. Beijing: Beijing Jiaotong University, 2021.
[18] 勾月凯, 代海涛, 董健, 等. 风力发电机组叶片智能巡检系统研究[C]//第五届中国风电后市场专题研讨会. 上海: [出版者不详], 2018: 203-208.
GOU Yuekai, DAI Haitao, DONG Jian, et al. Research on intelligent inspection system of wind turbine blades[C]//Proceedings of the 5th China Wind Power Post Market Symposium. Shanghai: [s,n.], 2018: 203-208.
[19] KANELLAKIS C, FRESK E, MANSOURI S S, et al. Towards visual inspection of wind turbines: a case of visual data acquisition using autonomous aerial robots[J]. IEEE access, 2020, 8: 181650–181661.
[20] GUO Haowen, CUI Qiangqiang, WANG Jinwang, et al. Detecting and positioning of wind turbine blade tips for UAV-based automatic inspection[C]//IGARSS 2019—2019 IEEE International Geoscience and Remote Sensing Symposium. Yokohama: IEEE, 2019: 1374-1377.
[21] MOOLAN-FEROZE O, KARACHALIOS K, NIKOLAIDIS D N, et al. Simultaneous drone localisation and wind turbine model fitting during autonomous surface inspection[C]//2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Macao: IEEE, 2019: 2014-2021.
[22] REDMON J, FARHADI A. YOLOv3: an incremental improvement[EB/OL]. (2018-04-08)[2022-04-11].https://arxiv.org/abs/1804.02767.
[23] HE Kaiming, ZHANG Xiangyu, REN Shaoqing, et al. Spatial pyramid pooling in deep convolutional networks for visual recognition[C]//European Conference on Computer Vision. Cham: Springer, 2014: 346-361.
[24] LIU Shu, QI Lu, QIN Haifang, et al. Path aggregation network for instance segmentation[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City: IEEE, 2018: 8759-8768.
[25] WANG C Y, BOCHKOVSKIY A, LIAO H Y M. Scaled-YOLOv4: scaling cross stage partial network[C]//2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Nashville: IEEE, 2021: 13024-13033.
Similar References:

Memo

-

Last Update: 1900-01-01

Copyright © CAAI Transactions on Intelligent Systems