基于融合重构的电气设备红外图像EnFCM聚类分割方法

刘沛津; 张香瑞; 魏平

基于融合重构的电气设备红外图像EnFCM聚类分割方法

刘沛津^1,,
张香瑞^1, ,,
魏平²

1.
西安建筑科技大学机电工程学院, 陕西西安 701055
2.
国家电网有限公司西北分部, 陕西西安 710048

基金项目:

国家自然科学基金 61903291

陕西省重点研发计划 2022GY-134

详细信息

作者简介:
刘沛津（1971-），女，博士，副教授，主要从事电力电子及电气故障诊断等方面的研究。E-mail：liuxpj@163.com

通讯作者:
张香瑞（1996-），男，硕士，主要从事红外图像处理和电气故障诊断方面的研究。E-mail：1078741460@qq.com

中图分类号: TM507;TP391.4
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- 文章访问数: 34
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- 被引次数: 0
出版历程
- 收稿日期: 2022-12-29
- 修回日期: 2023-03-09
- 刊出日期: 2024-03-20

EnFCM Clustering Segmentation Method for Infrared Image of Electrical Equipments Based on Fusion Reconstruction

1.
Mechanical and Electrical Engineering, Xi'an University of Architecture and Technology, Xi'an 710055, China
2.
Northwest Branch of State Grid Corporation of China, Xi'an 710048, China

摘要

摘要: 红外图像分割是电气设备红外故障诊断的关键环节，而电气设备的不均匀散热、较低的对比度与多源噪声的干扰，会导致目标区域过分割，严重影响分割精度。对此本文提出一种基于融合重构的EnFCM（Enhanced Fuzzy C-Means）聚类电气设备红外图像分割方法。首先对梯度图像进行自适应形态学重建操作，保证算法对噪声图像的分割能力；其次对图像进行显著性检测，将显著图与梯度图融合得到重构后的图像，凸显故障部位的特征，避免过分割；然后对重构后的图像进行分水岭分割获取超像素图像，最后对超像素图像直方图聚类得到分割结果。对电气设备红外图像的实验结果表明：本文算法在电气设备红外图像上能准确分割出故障区域，获取其位置与轮廓，有效改善了过分割现象，在选取的交并比与DICE系数指标对比中，本文方法对比选取的FRFCM、FCM、SFFCM、FCM_SICM、RSSFCA、AFCF平均提升了81%与79%；同时对噪声有较强的鲁棒性，在选取的分割准确率指标对比中，本文方法对比选取的FRFCM、FCM、SFFCM、FCM_SICM、RSSFCA、AFCF平均提升了73%，取得了较优的分割效果。
- 红外图像 /
- 图像分割 /
- 自适应形态学重建 /
- 显著性检测 /
- EnFCM
Abstract: Infrared image segmentation plays a pivotal role in diagnosing faults in electrical equipment using infrared imagery. However, uneven heat dissipation, lower contrast, and interference from multiple sources of noise in electrical equipment can lead to over-segmentation of the target region, seriously affecting segmentation accuracy. In this study, we propose an Enhanced Fuzzy C-Means (EnFCM) clustering method based on fusion reconstruction for infrared image segmentation of electrical equipment. First, the gradient image was subjected to an adaptive morphological reconstruction operation to ensure the segmentation ability of the algorithm on noisy images; second, the image was tested for saliency, and the reconstructed image was obtained by fusing the saliency map with the gradient map to highlight the features of the fault site and avoid over-segmentation; then, watershed segmentation was performed on the reconstructed image to obtain the super-pixel image; finally, the histogram clustering of the super-pixel image was obtained by segmentation. The experimental results on the infrared image of electrical equipment show that the algorithm in this paper can accurately segment the fault area on it, obtain its location and contour, and effectively improve the phenomenon of over-segmentation and in the comparison of the selected intersection and concatenation ratio and DICE coefficient indexes, this paper's method improves 81% and 79% on average compared to selected FRFCM, FCM, SFFCM, FCM_SICM, RSSFCA, and AFCF; meanwhile, it is extremely robust to noise, and in the comparison of selected segmentation accuracy indexes, this paper's method achieves segmentation results that are on average 73% superior compared to selected FRFCM, FCM, SFFCM, FCM_SICM, RSSFCA, and AFCF, thus, superior segmentation results were achieved.
- infrared image /
- image segmentation /
- adaptive morphological reconstruction /
- saliency detection /
- EnFCM

HTML全文

图 1 基于融合重构的EnFCM聚类分割方法整体框架

Figure 1. Integral framework of EnFCM cluster segmentation method based on fusion reconstruction

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图 2 自适应形态学重建去噪效果。(a)含噪图像；(b)去噪结果

Figure 2. Adaptive morphological gradient reconstruction denoising effect. (a) noisy image; (b) denoising result

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图 3 自适应形态学重建后梯度图像。(a)原梯度图像；(b)重建后的梯度图像

Figure 3. Gradient image after adaptive morphological reconstruction. (a) Original gradient image; (b) reconstructed gradient image

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图 4 FCM方法分割效果。(a)定子匝间短路；(c)转子断条故障；(b)、(d)分割结果

Figure 4. Segmentation effect of FCM method. (a) Stator turn-to-turn short-circuit fault; (c) fault of the rotor break; (b)(d) segmentation results

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图 5 融入了SR前后的梯度图像。(a)融入SR前的梯度；(b)融入SR后的梯度

Figure 5. Gradient images before and after SR are incorporated. (a) Gradient before integration into SR; (b) gradient after integration into SR

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图 6 定子匝间短路（含噪声）。(a)原图；(b)AFCF；(c)FRFCM；(d)FCM；(e)SFFCM；(f)FCM_SICM；(g)RSSFCA；(h)本文算法

Figure 6. Stator turn-to-turn short-circuit fault (Contains noise). (a) Original iamge; (b) AFCF; (c) FRFCM; (d) FCM; (e) SFFCM; (f) FCM_SICM; (g) RSSFCA; (h) proposed

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图 7 转子断条故障（含噪声）。(a)原图；(b)AFCF；(c)FRFCM；(d)FCM；(e)SFFCM；(f)FCM_SICM；(g)RSSFCA；(h)本文算法

Figure 7. Fault of the rotor break (Contains noise). (a) Original iamge; (b) AFCF; (c) FRFCM; (d) FCM; (e) SFFCM; (f) FCM_SICM; (g) RSSFCA; (h) proposed

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图 8 刀闸故障（含噪声）。(a)原图；(b)AFCF；(c)FRFCM；(d)FCM；(e)SFFCM；(f)FCM_SICM；(g)RSSFCA；(h)本文算法

Figure 8. Breaker failure (Contains noise). (a) Original iamge; (b) AFCF; (c) FRFCM; (d) FCM; (e) SFFCM; (f) FCM_SICM; (g) RSSFCA; (h) proposed

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图 9 线夹故障1（含噪声）。(a)原图；(b)AFCF；(c)FRFCM；(d)FCM；(e)SFFCM；(f)FCM_SICM；(g)RSSFCA；(h)本文算法

Figure 9. Contact terminal failure 1 (Contains noise). (a) Original iamge; (b) AFCF; (c) FRFCM; (d) FCM; (e) SFFCM; (f) FCM_SICM; (g) RSSFCA; (h) proposed

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图 10 线夹故障2（含噪声）。(a)原图；(b)AFCF；(c)FRFCM；(d)FCM；(e)SFFCM；(f)FCM_SICM；(g)RSSFCA；(h)本文算法

Figure 10. Contact terminal failure 2 (Contains noise). (a) Original iamge; (b) AFCF; (c) FRFCM; (d) FCM; (e) SFFCM; (f) FCM_SICM; (g) RSSFCA; (h) proposed

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图 11 电抗器故障（含噪声）。(a)原图；(b)AFCF；(c)FRFCM；(d)FCM；(e)SFFCM；(f)FCM_SICM；(g)RSSFCA；(h)本文算法

Figure 11. Inductor failure (Contains noise). (a) Original iamge; (b) AFCF; (c) FRFCM; (d) FCM; (e) SFFCM; (f) FCM_SICM; (g) RSSFCA; (h) proposed

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表 1 不同分割方法的IOU与DICE系数值

Table 1. IOU and DICE coefficient values of different segmentation methods

Experimental results	Index	Methods
Experimental results	Index	FRFCM	FCM	SFFCM	FCM_SICM	RSSFCA	AFCF	Proposed
Fig.6	IOU	0.5823	0.3658	0.4897	0.5431	0.3256	0.0827	0.9486
Fig.6	DICE	0.6521	0.4046	0.5215	0.6418	0.3391	0.8517	0.9587
Fig.7	IOU	0.7015	0.8019	0.7259	0.5681	0.4033	0.8002	0.9635
Fig.7	DICE	0.7825	0.8693	0.7652	0.5852	0.4231	0.8213	0.9635
Fig.8	IOU	0.8369	0.3845	0.3652	0.6485	0.4988	0.6378	0.9368
Fig.8	DICE	0.8722	0.4316	0.3596	0.6716	0.5021	0.6521	0.9465
Fig.9	IOU	0.8701	0.5864	0.3142	0.4163	0.4374	0.8115	0.9662
Fig.9	DICE	0.7711	0.6364	0.3212	0.4623	0.4516	0.8311	0.9732
Fig.10	IOU	0.8652	0.4562	0.3021	0.6523	0.6881	0.3225	0.9625
Fig.10	DICE	0.8699	0.4214	0.3124	0.7024	0.6557	0.3314	0.9718
Fig.11	IOU	0.8421	0.3022	0.6625	0.7968	0.3225	0.6337	0.9568
Fig.11	DICE	0.8235	0.3158	0.6538	0.8024	0.3126	0.6118	0.9589

下载: 导出CSV

表 2 不同方法的分割准确率（SA）值

Table 2. Segmentation accuracy (SA) values of different methods

Experimental results	FRFCM	FCM	SFFCM	FCM_SICM	RSSFCA	AFCF	Proposed
Fig. 6	0.7654	0.4254	0.6535	0.5674	0.4885	0.8005	0.9654
Fig. 7	0.8243	0.7635	0.7224	0.7423	0.7214	0.7968	0.9824
Fig. 8	0.8535	0.4575	0.4635	0.5663	0.8957	0.5671	0.9633
Fig. 9	0.3525	0.4012	0.8026	0.4685	0.7135	0.8213	0.9736
Fig. 10	0.8221	0.4115	0.4021	0.6587	0.5425	0.5732	0.9689
Fig. 11	0.8365	0.4965	0.4215	0.6652	0.4002	0.5235	0.9775

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