Recognition of Bird’s Nest on Transmission Tower in Aerial Image of High-volage Power Line by YOLOv3 Algorithm

Zhong Ying-chun; Sun Si-yu; Lyu Shuai; Luo Zhi-yong; Xiong Yong-liang; He Hui-qing

doi:10.12052/gdutxb.190128

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Recognition of Bird’s Nest on Transmission Tower in Aerial Image of High-volage Power Line by YOLOv3 Algorithm

更新时间：2026-05-27

- Recognition of Bird’s Nest on Transmission Tower in Aerial Image of High-volage Power Line by YOLOv3 Algorithm
- Vol. 37, Issue 3, Pages: 42-48(2020)
- 作者机构：
  
  1. 广东工业大学自动化学院,广东,广州,510006
  2. 广州市树根互联技术有限公司,广东,广州,510308
  3. 广州优飞科技有限公司,广东,广州,510830
  4. 国家电网江西省萍乡供电公司,江西,萍乡,330000
- 作者简介：
- 基金信息：
- DOI：10.12052/gdutxb.190128
  CLC： TP391.41
- Online First：12 May 2020，
  
  Published：2020
- 稿件说明：
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钟映春, 孙思语, 吕帅, 罗志勇, 熊勇良, 何惠清. 铁塔航拍图像中鸟巢的YOLOv3识别研究[J]. 广东工业大学学报, 2020, 37(3): 42-48.

Zhong Ying-chun, Sun Si-yu, Lyu Shuai, et al. Recognition of Bird’s Nest on Transmission Tower in Aerial Image of High-volage Power Line by YOLOv3 Algorithm[J]. 2020, 37(3): 42-48.
钟映春, 孙思语, 吕帅, 罗志勇, 熊勇良, 何惠清. 铁塔航拍图像中鸟巢的YOLOv3识别研究[J]. 广东工业大学学报, 2020, 37(3): 42-48. DOI： 10.12052/gdutxb.190128.

Zhong Ying-chun, Sun Si-yu, Lyu Shuai, et al. Recognition of Bird’s Nest on Transmission Tower in Aerial Image of High-volage Power Line by YOLOv3 Algorithm[J]. 2020, 37(3): 42-48. DOI： 10.12052/gdutxb.190128.

摘要

电力铁塔上的鸟巢、风筝等异物会严重影响电力架空输电线路的安全性。无人机在巡检过程中会针对电力铁塔进行专门拍照

检测识别铁塔上是否存在鸟巢等异物。针对经典YOLOv3算法在识别铁塔航拍图像中的鸟巢时存在识别精度不高、识别效率偏低、权重参数规模过大等不足

提出了改进方法。首先

设计了改进算法的总体架构

并构建了图像数据集; 其次

分别从预测框的宽高损失函数、预测类别不平衡损失函数和神经网络结构等3个方面对经典YOLOv3算法进行改进。实验结果表明

本文的改进措施切实有效

可以在提高识别精度的同时大幅度减小权重参数规模

且识别效率良好。此外

对YOLOv3的改进方法而言

改进其神经网络结构的效果明显好于其他改进措施

为将来在无人机巡检过程中实现实时检测识别目标物奠定了重要基础。

Abstract

The safety of high-voltage power line is usually threatened seriously by the foreign matters such as bird’s nests or kites and so on. The transmission tower is an important part of the system of high-voltage power line. So the UAV (Unmanned Aerial Vehicle) often takes photos of tower especially when inspecting the high-voltage power line and these photos usually have to be analyzed by the classical algorithm of YOLOv3 (You Only Look Once Version 3) whether they contain the foreign matters or not. This research is conducted to improve the classical algorithm of YOLOv3 in order to improve its precision and deficiency and unknown scale of weight parameters. Initially

the structure of improvement is designed and image data set is constructed. Second

the classical algorithm of YOLOv3 is improved from three ways: the width and height of loss functions of prediction box

the unbalanced loss function of prediction type and the network structure of classical algorithm are improved respectively. The experiments show that: the improvements proposed are effective

which improves the average recognition precision and reduces the scale of weight parameters greatly and maintains the efficiency. The result of improving the classical algorithm’s neural network structure is obviously better than other ways

which is probably the main direction to improving the algorithm. The investigation provides important basics to detect the objects on real time of UAV.

关键词

Keywords

references

ZHANG Z X. Design of large-span tower structure for transmission lines [J]. Heilongjiang Science, 2018, 9(20): 54-55.

WANG S H, YE Z Q. Analysis of bird damage accidents on overhead transmission lines and prevention techniques [J]. High Voltage Apparatus, 2011, 47(2): 61-67.

WANG H H, ZHANG J J. Discussion on the threat of birds to overhead transmission lines and countermeasures [J]. Management Technology of SME, 2012(7): 308-309.

HU Z M, LI K, TANG G F, et al . A new mode of transmission line unmanned aerial vehicle nest-nest inspection [J ] . Jiangxi Electric Power, 2018, 42(12): 13-15.

WANG J J, ZHANG W, LOU H H, et al . Power line detection method based on UAV image [J ] . Computer Systems Applications, 2019, 28(2): 233-239.

WU X, YUAN P, PENG Q, et al . Detection of bird nests in overhead catenary system images for high-speed rail [J ] . Pattern Recognition, 2016, 51: 242-254.

XU J, HAN J, TONG Z G, et al . Method for detecting bird’s nest on tower based on UAV image [J ] . Computer Engineering and Applications, 2017, 53(6): 231-235.

LEI X S, SUI Z H. Intelligent fault detection of high voltage line based on the faster R-CNN [J]. Measurement, 2019, 138: 379-385.

CHEN X, ZHANG J, CHEN W W, et al . Convolutional neural network algorithm and case [J ] . Journal of Guangdong University of Technology, 2017, 34(6): 20-26.

WEI W H, MA Q L, GAO Z L, et al . Application of improved convolutional neural network in image smoke detection [J ] . Intelligent Computer and Applications, 2019, 9(3): 40-44.

LIU M, WANG X T, ZHOU L, et al . Study on extraction and recognition of traditional Chinese medicine tongue manifestation: based on deep learning and migration learning [J ] . Journal of Traditional Chinese Medicine, 2019, 60(10): 835-840.

REDMON J, FARHADI A. Yolo9000: better, faster, stronger[C]//IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, Hawaii: IEEE, 2017: 7263-7271.

CHEN S D, WEI W, HE B Q, et al . Action recognition based on improved deep convolutional neural network [J ] . Application Research of Computers, 2019, 36(3): 945-949.

EUNSEOP L, DAIJIN K. Accurate traffic light detection using deep neural network with focal regression loss [J]. Image and Vision Computing, 2019, 87: 24-36.

HUANG G, LIU Z, MAATEN L, et al. Densely connected convolutional networks[C]//IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, Hawaii: IEEE, 2017: 4700-4708.

REN S, HE K, GIRSHICK R, et al . Object detection networks on convolutional feature maps [J ] . IEEE transactions on pattern analysis and machine intelligence, 2016, 39(7): 1476-1481.

YANG M J, SU C Y, CHEN J, et al . Loop closure detection for visual SLAM using convolutional neural networks [J ] . Journal of Guangdong University of Technology, 2018, 35(5): 31-37.

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