改进Chan-Vese模型的电力设备红外图像分割算法

张秋铭; 李云红; 罗雪敏; 屈海涛; 苏雪平; 任劼; 周小计

改进Chan-Vese模型的电力设备红外图像分割算法

张秋铭^{1, 2},
李云红^1, ,,
罗雪敏¹,
屈海涛³,
苏雪平¹,
任劼¹,
周小计¹

1.
西安工程大学电子信息学院, 陕西西安 710048
2.
国家电网四川省电力公司信息通信公司, 四川成都 610000
3.
枣庄学院, 山东枣庄 277160

基金项目:

国家自然科学基金 61902301

陕西省科技厅自然科学基础研究重点项目 2022JZ-35

陕西省教育厅自然科学基础研究计划 19JK0364

详细信息

作者简介:
张秋铭（1995-），女，助理工程师，研究方向是图像处理、计算机视觉

通讯作者:
李云红（1974-），女，教授，研究方向为红外热像技术、数字图像处理和信号与信息处理技术。E-mail：hitliyunhong@163.com

中图分类号: TP751.1
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- 文章访问数: 179
- HTML全文浏览量: 67
- PDF下载量: 48
出版历程
- 收稿日期: 2021-10-06
- 修回日期: 2021-11-09
- 刊出日期: 2023-02-19

Electric Equipment Infrared Image Segmentation Method Based on Improved Chan-Vese Model

1.
School of Electronics and Information, Xi'an Polytechnic University, Xi'an 710048, China
2.
Information Communication Company of Sichuan State Grid, Chengdu 610000, China
3.
Zaozhuang University, Zaozhaung 277160, China

摘要

摘要: 针对电力设备在线监测系统中红外图像分割效果差，速度慢等问题，提出一种改进的Chan-Vese模型的红外图像分割算法。首先，通过引入边缘能量项，一方面增强模型的局部控制能力，另一方面有效抑制了轮廓偏移。其次，利用径向基函数取代了传统的长度正则项，简化了计算。然后，通过引入内部能量项省去初始化过程，节省了算法的运行时间。经实验验证，Dice重合率（Dice similarity coefficient, DSC）平均值为0.9808，错误分割率（ratio of segmentation error, RSE）平均值为0.025，算法运行时间比其他模型总体平均值低66.8%。改进后的Chan-Vese模型分割算法的Dice重合率和错误分割率等均优于GAC-CV、CV-RSF、区域型水平集和Multiphase-CV模型分割算法。
- 红外图像分割 /
- Chan-Vese模型 /
- 边缘能量项 /
- 径向基函数
Abstract: To address the problems of poor infrared image segmentation and slow speed in the online monitoring system of power equipment, an improved infrared image segmentation algorithm based on the Chan-Vese model is proposed. First, by introducing the edge energy term, the local control ability of the model is enhanced and the contour shift is effectively suppressed. Second, a radial basis function is used to replace the traditional length regularization term, which simplifies the calculation. Subsequently, the initialization process is omitted by introducing internal energy items, which reduces the running time of the algorithm. After the experimental verification, the average DSC was 0.9808, the average value was 0.025, and the algorithm running time was 66.8% lower than the overall average of the other models. The improved Chan-Vese model segmentation algorithms DSC and RSE are better than the GAC-CV, CV-RSF, regional level set, and multiphase-CV model segmentation algorithms.
- infrared image segmentation /
- Chan-Vese model /
- edge energy term /
- radial basis function

HTML全文

图 1 Chan-Vese算法示意图

Figure 1. Schematic diagram of Chan-Vese algorithm

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图 2 轮廓偏移示意图

Figure 2. Schematic diagram of contour offset

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图 3 改进后Chan-Vese算法流程

Figure 3. Improved Chan-Vese algorithm flow chart

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图 4 管芯电阻分割对比

Figure 4. Die resistance segmentation comparison chart

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图 5 低压柜分割对比

Figure 5. Low-voltage cabinet segmentation comparison chart

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图 6 交直流接触器分割对比

Figure 6. AC and DC contactor segmentation comparison chart

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图 7 电容器组分割对比

Figure 7. Capacitor bank segmentation comparison chart

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图 8 电压互感器分割对比

Figure 8. Voltage transformer segmentation comparison chart

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图 9 中性点分割对比

Figure 9. Neutral point segmentation comparison chart

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表 1 DSC和RSE对比

Table 1 DSC and RSE comparison

	Fig.4		Fig.5		Fig.6		Fig.7		Fig.8		Fig.9
	DSC	RSE	DSC	RSE	DSC	RSE	DSC	RSE	DSC	RSE	DSC	RSE
Multiphase-CV	0.8754	0.0487	0.4383	0.5066	0.9733	0.0331	0.9123	0.1120	0.8532	0.1254	0.5031	0.4833
GAC-CV	0.8534	0.0425	0.6542	0.4524	0.9874	0.0324	0.9588	0.0421	0.4435	0.5673	0.9322	0.0695
CV-RSF	0.9789	0.0235	0.8821	0.3802	0.9325	0.0728	0.9614	0.0332	0.0233	0.8532	0.9475	0.0432
Region-based	0.9821	0.0232	0.3532	0.4244	0.9877	0.0242	0.9655	0.0310	0.9322	0.0614	0.9217	0.0782
Improved CV	0.9842	0.0217	0.9877	0.0232	0.9883	0.0237	0.9632	0.0315	0.9644	0.0223	0.9723	0.0334

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表 2 分割时间对比

Table 2 Split time comparison s

	Fig.4	Fig.5	Fig.6	Fig.7	Fig.8	Fig.9
Multiphase-CV	20.7789	15.7865	3.2275	10.7602	8.5132	6.7832
GAC-CV	10.8322	15.5173	0.3688	7.8112	7.3345	3.2342
CV-RSF	9.2303	0.1344	0.2376	10.3325	67.5434	5.2874
Region-based	7.2304	30.5542	0.0834	12.4723	9.3567	5.8723
Improved CV	7.0322	0.0831	0.0886	5.6632	3.2127	2.7545

下载: 导出CSV

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