scholarly journals Thermal Fault Detection and Diagnosis of Electrical Equipment Based on the Infrared Image Segmentation Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Hongzhao Li
Keyword(s):  
Image Segmentation ◽  
Fault Detection ◽  
Infrared Imaging ◽  
Infrared Image ◽  
Electrical Equipment ◽  
Fault Detection And Diagnosis ◽  
Segmentation Algorithm ◽  
Imaging Technology ◽  
Infrared Image Segmentation ◽  
Image Segmentation Algorithm

With the advancement of social economy, electricity has gradually entered thousands of households and become a commonly used energy source. However, it cannot be ignored that electricity is dangerous in itself and should be used rationally and effectively. The fault detection of power equipment has become a top priority because they are essential tools for storing, transmitting, and transferring electric power. Based on infrared imaging technology, the principle of infrared imaging technology is introduced in this paper, and effective diagnosis methods are analyzed and summarized in detail. The effectiveness of the proposed infrared image segmentation algorithm is verified through the practical application of the infrared image segmentation algorithm in the detection of interior and exterior faults of electrical equipment.

scholarly journals An Infrared Image Target Segmentation Based on Improved Threshold Method

2021 ◽  
Vol 15 ◽  
pp. 820-828
Author(s):  
Manzeng Ma ◽  
Dan Liu ◽  
Ruirui Zhang
Keyword(s):  
Image Segmentation ◽  
Target Detection ◽  
Infrared Imaging ◽  
Infrared Image ◽  
Operation Time ◽  
Optimal Threshold ◽  
Threshold Method ◽  
Intelligent Algorithms ◽  
Infrared Image Segmentation ◽  
Detection And Recognition

In recent years, infrared images have been applied in more and more extensive fields and the current research of infrared image segmentation and recognition can’t satisfy the needs of practical engineering applications. The interference of various factors on infrared detectors result in the targets detected presenting the targets of low contrast, low signal-to-noise ratio (SNR) and fuzzy edges on the infrared image, thus increasing the difficulty of target detection and recognition; therefore, it is the key point to segment the target in an accurate and complete manner when it comes to infrared target detection and recognition and it has great importance and practical value to make in-depth research in this respect. Intelligent algorithms have paved a new way for infrared image segmentation. To achieve target detection, segmentation, recognition and tracking with infrared imaging infrared thermography technology mainly analyzes such features as the grayscale, location and contour information of both background and target of infrared image, segments the target from the background with the help of various tools, extracts the corresponding target features and then proceeds recognition and tracking. To seek the optimal threshold of an image can be seen as to find the optimum value of a confinement problem. As to seek the threshold requires much computation, to seek the threshold through intelligent algorithms is more accurate. This paper proposes an automatic segmentation method for infrared target image based on differential evolution (DE) algorithm and OTSU. This proposed method not only takes into consideration the grayscale information of the image, but also pays attention to the relevant information of neighborhood space to facilitate more accurate image segmentation. After determining the scope of the optimal threshold, it integrates DE’s ability of globally searching the optimal solution. This method can lower the operation time and improve the segmentation efficiency. The simulation experiment proves that this method is very effective.

An Infrared Image Segmentation Algorithm Based on Spatial Correlative Information

2010 ◽  
Vol 44-47 ◽  
pp. 3274-3278
Author(s):  
Ke Yong Wang ◽  
Cheng Tian Song ◽  
Jia Hao Deng
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Image Segmentation ◽  
Adaptive Algorithm ◽  
Infrared Image ◽  
Infrared Images ◽  
Background Condition ◽  
Complex Background ◽  
Infrared Image Segmentation ◽  
Image Segmentation Algorithm

Image segmentation is an important technique for image processing and computer vision. The principles of 1-D Otsu’s algorithm and thresholding through index of fuzziness are described. Since the infrared images of tank have low object-background contrasts and blurred boundaries in the complex background condition, an adaptive algorithm for image thresholding through index of fuzziness, which is combined with the spatial correlative information, is proposed. The new method makes full use of the spatial correlation of pixels, so that it can extract the detail of the image from the complex background effectively, and improve the accuracy of the segmentation. The results of experiments prove that the presented algorithm has better performance and better robustness against noise.

scholarly journals Infrared image segmentation based on multi-information fused fuzzy clustering method for electrical equipment

2020 ◽  
Vol 17 (2) ◽  
pp. 172988142090960
Author(s):  
Can Qi ◽  
Qingwu Li ◽  
Yan Liu ◽  
Jinyan Ni ◽  
Ruxiang Ma ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Objective Function ◽  
Fuzzy Clustering ◽  
Infrared Image ◽  
Electrical Equipment ◽  
Segmentation Result ◽  
Clustering Method ◽  
Background Interference ◽  
Fuzzy Clustering Method ◽  
Infrared Image Segmentation

Serious noise pollution and background interference bring great difficulties to infrared image segmentation of electronic equipment. A novel infrared image segmentation method based on multi-information fused fuzzy clustering method is proposed in this article. Firstly, saliency detection is performed on the infrared image to obtain the saliency map, which determines the initial clustering center and enhances the contrast of the original infrared image. Secondly, the weighting exponent in the objective function is adjusted adaptively. Then local and global spatial constraints are added to the objective function of the fuzzy clustering method, which can reduce the noise and background interference. Finally, the Markov constrained field is calculated according to the initial segmentation result. After that the joint field of fuzzy clustering field and the Markov random field is constructed to obtain the optimized segmentation result. The algorithm is evaluated on the infrared images of electrical equipment, and the experimental results show that the proposed method is robust to noise and complicated background. Compared with other methods, the proposed method improves the average segmentation accuracy and T measure by about 10% and 13%.

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