基于简单线性迭代聚类优化的无人机图像去雾算法及其在风电场中的应用

doi:10.12096/j.2096-4528.pgt.20006

发电技术 ›› 2020, Vol. 41 ›› Issue (6): 608-616.DOI: 10.12096/j.2096-4528.pgt.20006

基于简单线性迭代聚类优化的无人机图像去雾算法及其在风电场中的应用

刘厦¹^,³(), 孙哲²(), 仇梓峰¹^,³^,*(), 胡炎³()

¹ 中国电子科技集团公司第五十四研究所, 河北省石家庄市 050081
² 海装驻邯郸地区军事代表室, 河北省石家庄市 050081
³ 中国电子科技集团公司航天信息应用技术重点实验室, 河北省石家庄市 050081

收稿日期:2020-03-17 出版日期:2020-12-31 发布日期:2021-01-12
通讯作者: 仇梓峰
作者简介:仇梓峰(1993), 男, 硕士, 工程师, 研究方向为基于无人机的图像处理, 本文通信作者, qzf93@qq.com
刘厦(1986), 男, 硕士, 工程师, 研究方向为无人机智能测控, ykbrjslj@sina.cn
孙哲(1983), 男, 工程师, 研究方向为计算机视觉, power61104@163.com
胡炎(1991), 男, 硕士, 工程师, 研究方向为模式识别、健康管理及机器学习, huyantju@126.com
基金资助:
国家重点研发计划项目(2017YFB0503003)

Unmanned Aerial Vehicle Image Dehazing Algorithm Based on Simple Linear Iterative Clustering Optimization and Its Application in Wind Farm

Sha LIU¹^,³(), Zhe SUN²(), Zifeng QIU¹^,³^,*(), Yan HU³()

¹ The 54 th Research Institute of CETC, Shijiazhuang 050081, Hebei Province, China
² Military Representatives Office of NED in Handan, Shijiazhuang 050081, Hebei Province, China
³ Key Laboratory of Aerospace Information Applications of CETC, Shijiazhuang 050081, Hebei Province, China

Received:2020-03-17 Published:2020-12-31 Online:2021-01-12
Contact: Zifeng QIU
Supported by:
National Key Research & Development Program of China(2017YFB0503003)

摘要/Abstract

摘要：

针对电力巡检过程中无人机（unmanned aerial vehicle，UAV）载荷因受雾气颗粒影响而导致UAV图像不清晰的问题，提出一种基于简单线性迭代聚类（simple linear iterative clustering，SLIC）优化的UAV图像去雾算法。通过雾天成像物理模型、暗通道先验定律、同质滤波与SLIC算法，改善电力巡检图像中白色区域及不均匀光照的影响，提升UAV图像去雾处理的效率，并对大气光强度参数进行自适应计算，以防止去雾过程中复原失真。实验结果表明提出的算法可有效恢复电力巡检图像的原始细节，并通过主观视觉评估及融合多种客观评价指标的对比，说明该算法相对于传统算法的优越性。

关键词: 风电场, 电力巡检, 图像去雾, 同质滤波, 简单线性迭代聚类(SLIC)

Abstract:

In order to solve the problem that the unmanned aerial vehicle (UAV) image is not clear due to the influence of fog particles, a UAV image dehazing algorithm based on simple linear iterative clustering (SLIC) optimization was proposed. Through the physical model of fog imaging, the prior law of dark channel, homogeneous filtering and SLIC algorithm, the influence of white area and uneven light in power inspection image was improved, the efficiency of UAV image dehazing was improved, and the adaptive calculation of atmospheric light intensity parameters was carried out to prevent the distortion in the dehazing process. The experimental results show that the algorithm can effectively restore the original details of the power inspection image. Through the comparison of subjective visual evaluation and fusion of a variety of objective evaluation indexes, it shows the superiority of the algorithm compared with the traditional algorithms.

Key words: wind farm, power inspection, image dehazing, homogeneous filtering, simple linear iterative clustering (SLIC)

中图分类号:

刘厦, 孙哲, 仇梓峰, 胡炎. 基于简单线性迭代聚类优化的无人机图像去雾算法及其在风电场中的应用[J]. 发电技术, 2020, 41(6): 608-616.

Sha LIU, Zhe SUN, Zifeng QIU, Yan HU. Unmanned Aerial Vehicle Image Dehazing Algorithm Based on Simple Linear Iterative Clustering Optimization and Its Application in Wind Farm[J]. Power Generation Technology, 2020, 41(6): 608-616.

图/表 7

图1 同质滤波效果对比

Fig.1 Comparison of homogeneous filtering effect

图2 SLIC优化效果

Fig.2 SLIC optimization effect

图3 基于SLIC优化的UAV图像去雾算法流程图

Fig.3 Flow chart of UAV image dehazing algorithm based on SLIC optimization

图4 输电线路去雾处理结果

Fig.4 Dehazing results of transmission lines

图5 风力发电机去雾结果

Fig.5 Dehazing results of wind turbines

表1 3种算法的去雾性能客观评估

Tab. 1 Objective evaluation of dehazing performance of three algorithms

图像	算法	C	K	E_N
图 4左	本文算法	42.56	1.31	9.58
	HE算法	33.49	1.42	7.69
	PEI算法	38.54	1.58	8.03
图 4右	本文算法	43.14	1.67	8.76
	HE算法	31.57	1.96	7.01
	PEI算法	33.25	1.84	7.21
图 5	本文算法	55.87	0.89	10.23
	HE算法	42.36	0.86	8.25
	PEI算法	43.61	1.02	8.67

表2 3种算法的去雾处理时间

Tab. 2 Processing time of dehazing performance of three algorithms s

图像	本文算法	HE算法	PEI算法
图 4左	0.96	1.52	1.41
图 4右	1.02	1.67	1.38
图 5	2.57	4.71	4.02

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基于简单线性迭代聚类优化的无人机图像去雾算法及其在风电场中的应用

Unmanned Aerial Vehicle Image Dehazing Algorithm Based on Simple Linear Iterative Clustering Optimization and Its Application in Wind Farm

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