基于热成像的渗漏源检测

杨羽; 贺超广; 涂圆; 赵杰锋; 周晓萍; 唐立军

基于热成像的渗漏源检测

杨羽^{1, 2},
贺超广^{1, 2},
涂圆^{1, 2},
赵杰锋^{1, 2},
周晓萍^{1, 2, ,},
唐立军^{1, 2, ,}

1.
长沙理工大学物理与电子科学学院, 湖南长沙 410114
2.
近地空间电磁环境监测与建模湖南省普通高校重点实验室, 湖南长沙 410114

基金项目:

国家级大学生创业实践项目 S201910536003S

湖南省重点研发计划项目 2018GK2054

详细信息

作者简介:
杨羽（1999-），男，本科生，研究方向信号检测与处理研究

通讯作者:
周晓萍（1973-），女，讲师，研究方向信号检测与处理。E-mail：zhouxiaoping1000@126.com

唐立军（1963-），男，教授，博士生导师，研究方向为信号检测与处理研究。E-mail：tanglj@csust.edu.cn

中图分类号: TN219
计量
- 文章访问数: 102
- HTML全文浏览量: 34
- PDF下载量: 28
- 被引次数: 0
出版历程
- 收稿日期: 2021-05-26
- 修回日期: 2021-08-06
- 刊出日期: 2022-07-20

Leakage Source Detection Based on Thermal Imaging

YANG Yu^{1, 2},
HE Chaoguang^{1, 2},
TU Yuan^{1, 2},
ZHAO Jiefeng^{1, 2},
ZHOU Xiaoping^{1, 2
, ,},
TANG Lijun^{1, 2
, ,}

1.
Changsha University of Science and Technology, School of Physics and Electronic Science, Changsha 410114, China
2.
Key Laboratory of Electromagnetic Environment Monitoring and Modeling in Hunan Province, Changsha 410114, China

摘要

摘要: 针对屋面渗漏源难以检测的问题，研究了基于渗漏区域红外图像特征的灰度分段映射图像增强方法，提出了一种基于样板矩阵的图像快速识别技术，设计了一个屋面全自动渗漏源检测系统。在5 m×3 m屋面设置渗漏源形成多个渗漏区域，采用Mecanum轮小车搭载该系统对渗漏源进行检测，结果表明，该系统可以在89 s之内完成检测工作，总测试150个次渗漏点，漏测12个次渗漏点，识别准确率大于90%。该技术检测效率高、操作简单，配合相应载体可用于各类不明渗水源检测。
- 红外热成像 /
- 渗漏源检测 /
- 图像识别 /
- 灰度分段
Abstract: To address the difficulty in detecting the source of roof leakage, an image enhancement method that uses the infrared image features of the leakage area was studied using gray segmentation mapping. Rapid image recognition technology based on a template matrix was proposed, and an automatic roof leakage source detection system was designed. Leakage sources were set on a 5 m× 3 m roof to form multiple leakage areas. A mecanum wheeled trolley was used to support the system while detecting these sources. The results showed that the system could complete detection within 89 s, with a total of 150 leakage points tested and 12 leakage points missed, and the identification accuracy was greater than 90%. This technology has high detection efficiency and simple operation and can be used to detect all types of unknown water seepage sources with the corresponding carrier.
- infrared thermal imaging /
- leakage source detection /
- image recognition /
- gray-scale segmentation

HTML全文

图 1 灰度分段线性映射

Figure 1. Grayscale piecewise linear mapping

下载: 全尺寸图片幻灯片

图 2 样板矩阵和样板图像

Figure 2. Template matrix and template image

下载: 全尺寸图片幻灯片

图 3 渗漏区域红外图像

Figure 3. An infrared image of leakage area

下载: 全尺寸图片幻灯片

图 4 图像识别处理流程

Figure 4. Image recognition process

下载: 全尺寸图片幻灯片

图 5 搜寻轨迹

Figure 5. Search trajectory

下载: 全尺寸图片幻灯片

图 6 硬件系统组成结构图

Figure 6. Hardware system composition structure diagram

下载: 全尺寸图片幻灯片

图 7 系统软件流程

Figure 7. System software flow

下载: 全尺寸图片幻灯片

图 8 算法处理不同顺序结果对比图

Figure 8. Comparison of algorithm processing results in different sequences

下载: 全尺寸图片幻灯片

图 9 Mecanum轮实物图

Figure 9. Actual picture of Mecanum wheel

下载: 全尺寸图片幻灯片

图 10 具体运动分量

Figure 10. Specific motion components

下载: 全尺寸图片幻灯片

表 1 各种图像增强算法的峰值信噪比及其平均值

Table 1. Peak Signal to Noise Ratio of various image enhancement algorithms

	Nearest neighbor interpolate-on	Bicubic interpolate-on	Enhancement method in this paper
Fan	34.8049	36.1934	36.7289
Hand	33.6161	34.8478	35.2925
Lenna	28.9225	30.1287	30.4744
Cameraman	30.8624	32.311	32.6554
Sponge	33.0744	35.191	36.3204

下载: 导出CSV

表 2 各种图像增强算法的峰值信噪比平均值

Table 2. Average values of PSNR of various image enhancement algorithms

	Nearest neighbor interpolation	Bicubic interpolation	Enhancement method in this paper
Average peak SNR	32.25	33.73	34.29

下载: 导出CSV

表 3 算法处理先后顺序的区分差异

Table 3. Algorithm processing sequence difference

	PSNR	SSIM
Pseudo - color processing followed by interpolation amplification	19.9055	0.9399
Grayscale interpolation and amplification followed by pseudo-color processing	23.3838	0.9805

下载: 导出CSV

表 4 不同算法处理红外识别问题的时间

Table 4. Time for different algorithms to deal with infrared recognition problems

	Hough transform	Multi-angle infrared image target recognition method	This paper proposes a fast recognition algorithm
Accuracy/%	76.4	95.2	86.9
The average time/ms	214	127	32

下载: 导出CSV

表 5 系统测试结果-1个渗漏源

Table 5. System test results -1 leakage source

Times	Time -consuming /s	Results
1	70	√
2	65	√
3	74	√
4	71	√
5	73	×
6	78	√
7	67	√
8	64	√
9	72	√
10	63	√
11	61	√
12	81	√
13	81	√
14	61	×
15	68	√
16	61	√
17	78	√
18	83	√
19	81	√
20	58	√
21	64	√
22	62	√
23	67	√
24	59	√
25	68	√

下载: 导出CSV

表 6 系统测试结果-2个渗漏源

Table 6. System test results -2 leakage sources

Times	Time - consuming/s	The source 1 results	The source 2 results
1	72	√	√
2	76	√	√
3	81	√	√
4	75	×	√
5	74	√	√
6	73	√	√
7	68	√	√
8	84	√	√
9	72	×	√
10	71	√	√
11	69	√	√
12	76	√	√
13	73	√	√
14	67	√	×
15	81	√	√
16	88	×	√
17	81	√	√
18	73	√	√
19	72	√	√
20	71	√	√
21	68	√	√
22	67	√	√
23	69	√	√
24	62	√	×
25	66	√	√

下载: 导出CSV

表 7 系统测试结果-3个渗漏源

Table 7. System test results -3 leakage sources

Times	Time - consuming/s	The source 1 results	The source 2 results	The source 3 results
1	81	√	√	√
2	82	√	√	×
3	89	√	√	×
4	75	√	√	√
5	71	√	√	√
6	65	√	√	√
7	72	√	√	√
8	73	√	√	√
9	87	√	√	√
10	81	×	√	√
11	59	√	√	√
12	62	√	√	√
13	76	√	√	√
14	71	√	√	√
15	69	√	√	√
16	67	√	√	√
17	81	√	√	√
18	62	√	√	√
19	69	√	√	√
20	76	√	√	√
21	77	√	√	√
22	80	√	×	√
23	85	√	√	√
24	74	√	√	√
25	73	√	√	×

下载: 导出CSV

参考文献(7)

[1]	LIU Dunnan, HOU Rui, LI Qiang, et al. Research on fuzzy enhancement algorithms for infrared image recognition quality of power internet of things equipment based on membership function[J]. Journal of Visual Communication and Image Representation, 2019, 62(1): 359-367.
[2]	ZHENG Zhaoping, ZENG Hansheng, DING Cuijiao, et al. Summary on the infrared thermal imaging temperature measurement technology and its application[J]. Infrared Technology, 2003, 25(1): 96-98.
[3]	赵璐, 熊森. 多视角红外图像目标识别方法[J]. 红外与激光工程, 2021, 50(11): 403-408. https://www.cnki.com.cn/Article/CJFDTOTAL-HWYJ202111051.htm ZHAO Lu, XIONG Sen. Multi-view infrared image target recognition method[J]. Infrared and Laser Engineering, 2021, 50(11): 403-408. https://www.cnki.com.cn/Article/CJFDTOTAL-HWYJ202111051.htm
[4]	BAI Honggang, JIN Yingji. Application of infrared technology in military[J]. Journal of Engineering College of Armed Police Force, 2008(6): 5-7.
[5]	蔡毅, 王岭雪. 红外成像技术中的9个问题[J]. 红外技术, 2013, 35(11): 671-682. doi: 10.11846/j.issn.1001_8891.201311001 CAI Yi, WANG Lingxue. Nine problems in infrared imaging technology [J]. Infrared Technology, 2013, 35(11): 671-682. doi: 10.11846/j.issn.1001_8891.201311001
[6]	杨凯斌, 吴定祥, 龚金成, 等. 圆环陶瓷检测算法研究[J]. 中国陶瓷, 2017, 53(3): 39-44. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGTC201703008.htm YANG Kaibin, WU Dingxiang, GONG Jincheng, et al. Research on detection algorithm of ring ceramics [J]. China Ceramics, 2017, 53(3): 39-44. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGTC201703008.htm
[7]	Alwan Hassan M, Volkov A N, Shbani A. Solution of inverse and forward kinematics problems for mobile robot with six mecanum wheels[C]//IOP Conference Series: Materials Science and Engineering, 2021, 1094(1): 012071.