MSIS: Multispectral Instance Segmentation Method for Power Equipment

Infrared image of power equipment is widely used in power equipment fault detection, and segmentation of infrared images is an important step in power equipment thermal fault detection. Nevertheless, since the overlap of the equipment, the complex background, and the low contrast of the infrared image, the current method still cannot complete the detection and segmentation of the power equipment well. To better segment the power equipment in the infrared image, in this paper, a multispectral instance segmentation (MSIS) based on SOLOv2 is designed, which is an end-to-end and single-stage network. First, we provide a novel structure of multispectral feature extraction, which can simultaneously obtain rich features in visible images and infrared images. Secondly, a module of feature fusion (MARFN) has been constructed to fully obtain fusion features. Finally, the combination of multispectral feature extraction, the module of feature fusion (MARFN), and instance segmentation (SOLOv2) realize multispectral instance segmentation of power equipment. The experimental results show that the proposed MSIS model has an excellent performance in the instance segmentation of power equipment. The MSIS based on ResNet-50 has 40.06% AP.

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Research on Thermal Fault Detection Technology of Power Equipment based on Infrared Image Analysis

2018 IEEE 3rd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC) ◽

10.1109/iaeac.2018.8577908 ◽

2018 ◽

Cited By ~ 1

Author(s):

Lu Zhu-Mao ◽

Liu Qing ◽

Jin Tao ◽

Liu Yong-Xin ◽

Han Yu ◽

...

Keyword(s):

Image Analysis ◽

Fault Detection ◽

Infrared Image ◽

Power Equipment ◽

Detection Technology

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Insulator Contamination Perception Based on Feature Fusion of Infrared Image and Meteorological Parameters

Frontiers in Energy Research ◽

10.3389/fenrg.2021.746378 ◽

2021 ◽

Vol 9 ◽

Author(s):

Hongxia Wang ◽

Bo Wang ◽

Min Li ◽

Peng Luo ◽

Hengrui Ma ◽

...

Keyword(s):

Feature Extraction ◽

Meteorological Parameters ◽

Feature Fusion ◽

Low Cost ◽

Infrared Image ◽

Weather Conditions ◽

Power Grids ◽

Pollution Level ◽

Stable Operation ◽

Fusion Model

Polluted insulators seriously threaten the safe and stable operation of power grids, which attaches great significance to insulator contamination perception. Among the present methods, the non-contact approaches based on infrared images have gradually been widely used, as they are much more safe and are of low cost. However, the thermal effect of insulators is largely affected by meteorological conditions, which makes the infrared image-based methods less accurate. To solve the above problem, we take infrared image and meteorological parameters including humidity and temperature as input, and propose a feature fusion model to perceive insulator contamination in different weather conditions. Firstly, different feature extraction networks are used to perform feature extraction on the two types of data; secondly, the two features are concatenated to fuse together; thirdly, further feature extraction is performed and contamination is classified according to the pollution severity. Case studies show that the proposed method can better explore the relationship between humidity, temperature and pollution level of the insulators, thus can better separate the contamination grades and outperform the conventional infrared image based methods.

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MS-AFF: A Novel Semantic Segmentation Approach for Buried Object Based on Multi-scale Attentional Feature Fusion

10.21203/rs.3.rs-193757/v1 ◽

2021 ◽

Author(s):

Chao Lu ◽

Fansheng Chen ◽

Xiaofeng Su ◽

Dan Zeng

Keyword(s):

Deep Learning ◽

Spatial Information ◽

Feature Fusion ◽

Infrared Image ◽

Semantic Segmentation ◽

Target Object ◽

Infrared Images ◽

Feature Maps ◽

Multi Scale ◽

Visible Images

Abstract Infrared technology is a widely used in precision guidance and mine detection since it can capture the heat radiated outward from the target object. We use infrared (IR) thermography to get the infrared image of the buried obje cts. Compared to the visible images, infrared images present poor resolution, low contrast, and fuzzy visual effect, which make it difficult to segment the target object, specifically in the complex backgrounds. In this condition, traditional segmentation methods cannot perform well in infrared images since they are easily disturbed by the noise and non-target objects in the images. With the advance of deep convolutional neural network (CNN), the deep learning-based methods have made significant improvements in semantic segmentation task. However, few of them research Infrared image semantic segmentation, which is a more challenging scenario compared to visible images. Moreover, the lack of an Infrared image dataset is also a problem for current methods based on deep learning. We raise a multi-scale attentional feature fusion (MS-AFF) module for infrared image semantic segmentation to solve this problem. Precisely, we integrate a series of feature maps from different levels by an atrous spatial pyramid structure. In this way, the model can obtain rich representation ability on the infrared images. Besides, a global spatial information attention module is employed to let the model focus on the target region and reduce disturbance in infrared images' background. In addition, we propose an infrared segmentation dataset based on the infrared thermal imaging system. Extensive experiments conducted in the infrared image segmentation dataset show the superiority of our method.

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Entropy and Contrast Enhancement of Infrared Thermal Images Using the Multiscale Top-Hat Transform

Entropy ◽

10.3390/e21030244 ◽

2019 ◽

Vol 21 (3) ◽

pp. 244 ◽

Cited By ~ 9

Author(s):

Julio Mello Román ◽

José Vázquez Noguera ◽

Horacio Legal-Ayala ◽

Diego Pinto-Roa ◽

Santiago Gomez-Guerrero ◽

...

Keyword(s):

Multiple Scales ◽

Infrared Image ◽

Infrared Images ◽

Public Database ◽

Human Visual Perception ◽

Multiple Features ◽

Thermal Images ◽

Low Contrast ◽

Database Evaluation ◽

Infrared Thermal Images

Discrete entropy is used to measure the content of an image, where a higher value indicates an image with richer details. Infrared images are capable of revealing important hidden targets. The disadvantage of this type of image is that their low contrast and level of detail are not consistent with human visual perception. These problems can be caused by variations of the environment or by limitations of the cameras that capture the images. In this work we propose a method that improves the details of infrared images, increasing their entropy, preserving their natural appearance, and enhancing contrast. The proposed method extracts multiple features of brightness and darkness from the infrared image. This is done by means of the multiscale top-hat transform. To improve the infrared image, multiple scales are added to the bright areas and multiple areas of darkness are subtracted. The method was tested with 450 infrared thermal images from a public database. Evaluation of the experimental results shows that the proposed method improves the details of the image by increasing entropy, also preserving natural appearance and enhancing the contrast of infrared thermal images.

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Research on Fault Detection System of Power Equipment Based on UV and Infrared Image

IOP Conference Series Materials Science and Engineering ◽

10.1088/1757-899x/231/1/012062 ◽

2017 ◽

Vol 231 ◽

pp. 012062

Author(s):

Qiyu Lu ◽

Kun Ding

Keyword(s):

Fault Detection ◽

Detection System ◽

Infrared Image ◽

Power Equipment

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Infrared Electric Image Enhancement Based On Fuzzy Renyi Entropy and Quantum Genetic Algorithm

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.66-68.1774 ◽

2011 ◽

Vol 66-68 ◽

pp. 1774-1780

Author(s):

Song Hai Fan ◽

Shu Hong Yang ◽

Pu He ◽

Hong Yu Nie

Keyword(s):

Genetic Algorithm ◽

Infrared Image ◽

Renyi Entropy ◽

Rényi Entropy ◽

Maximal Entropy ◽

Infrared Images ◽

Low Contrast ◽

Quantum Genetic Algorithm ◽

Entropy Principle ◽

Electric Equipment

Infrared thermograph has been applied in electric equipment inspection widely, but the visual effects of infrared images are always undesirable. Considering the limitation of low luminance，low contrast in infrared images，an enhancement method based on fuzzy Renyi entropy and quantum genetic algorithm is presented in this paper．Firstly，the contrast-sketching function presented in [1] is improved based on the idea of segmentation. Then, in order to segment the infrared image, Renyi entropy is extend to fuzzy domain considering the fuzzy nature of infrared image, and is employed to threshold the infrared image following maximal entropy principle. In order to meet the real-time demand of online monitoring, quantum genetic algorithm is employed to search the optimal parameters of the transform function. The experimental results indicate that the method can well improve the visual effect of infrared electric images．

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The Research of Non-Uniformity Correction of Infrared Image in Thermal Wave Testing Based on Homomorphism Technology

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.58-60.2376 ◽

2011 ◽

Vol 58-60 ◽

pp. 2376-2380

Author(s):

Yuan Jia Song ◽

Wei Zhang ◽

Zheng Wei Yang ◽

Guo Feng Jin

Keyword(s):

Thermal Wave ◽

Signal To Noise Ratio ◽

Infrared Image ◽

Future Research ◽

Infrared Images ◽

Signal To Noise ◽

Improve Image Quality ◽

High Noise ◽

Low Contrast ◽

Homomorphic Filtering

The infrared thermal wave technology is a new nondestructive testing (NDT) method with a kind of advantage, including non-contact, intuitionistic, fast et al. But the infrared images always have defects that the low-contrast and high-noise due to uneven brightness and calefaction in the testing process, which enhance the difficulty of following quantitative distinguishment of defects. Therefore, the improved homomorphic filtering is given in this article. The detailed processes of the method and testing results are given. The results of the experiments show that the method has higher peak signal to noise ratio (PSNR), can improve image quality, which establish basis for future research of image segmentation.

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Gradient Ascent Optimization for Fault Detection in Electrical Power System Based on Wavelet Transformation

Current Signal Transduction Therapy ◽

10.2174/1574362414666190619092910 ◽

2019 ◽

Vol 14 ◽

Author(s):

Iyappan Murugesan ◽

Karpagam Sathish

Keyword(s):

Feature Extraction ◽

Fault Detection ◽

Power System ◽

Electrical Power ◽

Daubechies Wavelet ◽

Electrical Power System ◽

Gradient Ascent ◽

Neural Learning ◽

Weight Value ◽

Transmission And Distribution

: This paper presents electrical power system comprises many complex and interrelating elements that are susceptible to the disturbance or electrical fault. The faults in electrical power system transmission line (TL) are detected and classified. But, the existing techniques like artificial neural network (ANN) failed to improve the Fault Detection (FD) performance during transmission and distribution. In order to reduce the power loss rate (PLR), Daubechies Wavelet Transform based Gradient Ascent Deep Neural Learning (DWT-GADNL) Technique is introduced for FDin electrical power sub-station. DWT-GADNL Technique comprises three step, normalization, feature extraction and FD through optimization. Initially sample power TL signal is taken. After that in first step, min-max normalization process is carried out to estimate the various rated values of transmission lines. Then in second step, Daubechies Wavelet Transform (DWT) is employed for decomposition of normalized TLsignal to different components for feature extraction with higher accuracy. Finally in third step, Gradient Ascent Deep Neural Learning is an optimization process for detecting the local maximum (i.e., fault) from the extracted values with help of error function and weight value. When maximum error with low weight value is identified, the fault is detected with lesser time consumption. DWT-GADNL Technique is measured with PLR, feature extraction accuracy (FEA), and fault detection time (FDT). The simulation result shows that DWT-GADNL Technique is able to improve the performance of FEA and reduces FDT and PLR during the transmission and distribution when compared to state-of-the-art works.

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