Optimized OTSU Segmentation Algorithm-Based Temperature Feature Extraction Method for Infrared Images of Electrical Equipment
Infrared image processing is an effective method for diagnosing faults in electrical equipment, in which target device segmentation and temperature feature extraction are key steps. Target device segmentation separates the device to be diagnosed from the image, while temperature feature extraction a...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-02-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/24/4/1126 |
| _version_ | 1797297042532335616 |
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| author | Xueli Liu Zhanlong Zhang Yuefeng Hao Hui Zhao Yu Yang |
| author_facet | Xueli Liu Zhanlong Zhang Yuefeng Hao Hui Zhao Yu Yang |
| author_sort | Xueli Liu |
| collection | DOAJ |
| description | Infrared image processing is an effective method for diagnosing faults in electrical equipment, in which target device segmentation and temperature feature extraction are key steps. Target device segmentation separates the device to be diagnosed from the image, while temperature feature extraction analyzes whether the device is overheating and has potential faults. However, the segmentation of infrared images of electrical equipment is slow due to issues such as high computational complexity, and the temperature information extracted lacks accuracy due to the insufficient consideration of the non-linear relationship between the image grayscale and temperature. Therefore, in this study, we propose an optimized maximum between-class variance thresholding method (OTSU) segmentation algorithm based on the Gray Wolf Optimization (GWO) algorithm, which accelerates the segmentation speed by optimizing the threshold determination process using OTSU. The experimental results show that compared to the non-optimized method, the optimized segmentation method increases the threshold calculation time by more than 83.99% while maintaining similar segmentation results. Based on this, to address the issue of insufficient accuracy in temperature feature extraction, we propose a temperature value extraction method for infrared images based on the K-nearest neighbor (KNN) algorithm. The experimental results demonstrate that compared to traditional linear methods, this method achieves a 73.68% improvement in the maximum residual absolute value of the extracted temperature values and a 78.95% improvement in the average residual absolute value. |
| first_indexed | 2024-03-07T22:15:30Z |
| format | Article |
| id | doaj.art-fd4e642de7114bbca4fcd6c029d90fc3 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-07T22:15:30Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-fd4e642de7114bbca4fcd6c029d90fc32024-02-23T15:33:39ZengMDPI AGSensors1424-82202024-02-01244112610.3390/s24041126Optimized OTSU Segmentation Algorithm-Based Temperature Feature Extraction Method for Infrared Images of Electrical EquipmentXueli Liu0Zhanlong Zhang1Yuefeng Hao2Hui Zhao3Yu Yang4School of Electrical Engineering, Chongqing University, Chongqing 400044, ChinaSchool of Electrical Engineering, Chongqing University, Chongqing 400044, ChinaSchool of Electrical Engineering, Chongqing University, Chongqing 400044, ChinaSchool of Electrical Engineering, Chongqing University, Chongqing 400044, ChinaSchool of Electrical Engineering, Chongqing University, Chongqing 400044, ChinaInfrared image processing is an effective method for diagnosing faults in electrical equipment, in which target device segmentation and temperature feature extraction are key steps. Target device segmentation separates the device to be diagnosed from the image, while temperature feature extraction analyzes whether the device is overheating and has potential faults. However, the segmentation of infrared images of electrical equipment is slow due to issues such as high computational complexity, and the temperature information extracted lacks accuracy due to the insufficient consideration of the non-linear relationship between the image grayscale and temperature. Therefore, in this study, we propose an optimized maximum between-class variance thresholding method (OTSU) segmentation algorithm based on the Gray Wolf Optimization (GWO) algorithm, which accelerates the segmentation speed by optimizing the threshold determination process using OTSU. The experimental results show that compared to the non-optimized method, the optimized segmentation method increases the threshold calculation time by more than 83.99% while maintaining similar segmentation results. Based on this, to address the issue of insufficient accuracy in temperature feature extraction, we propose a temperature value extraction method for infrared images based on the K-nearest neighbor (KNN) algorithm. The experimental results demonstrate that compared to traditional linear methods, this method achieves a 73.68% improvement in the maximum residual absolute value of the extracted temperature values and a 78.95% improvement in the average residual absolute value.https://www.mdpi.com/1424-8220/24/4/1126power equipmentinfrared imagesegmentationtemperature feature extraction |
| spellingShingle | Xueli Liu Zhanlong Zhang Yuefeng Hao Hui Zhao Yu Yang Optimized OTSU Segmentation Algorithm-Based Temperature Feature Extraction Method for Infrared Images of Electrical Equipment Sensors power equipment infrared image segmentation temperature feature extraction |
| title | Optimized OTSU Segmentation Algorithm-Based Temperature Feature Extraction Method for Infrared Images of Electrical Equipment |
| title_full | Optimized OTSU Segmentation Algorithm-Based Temperature Feature Extraction Method for Infrared Images of Electrical Equipment |
| title_fullStr | Optimized OTSU Segmentation Algorithm-Based Temperature Feature Extraction Method for Infrared Images of Electrical Equipment |
| title_full_unstemmed | Optimized OTSU Segmentation Algorithm-Based Temperature Feature Extraction Method for Infrared Images of Electrical Equipment |
| title_short | Optimized OTSU Segmentation Algorithm-Based Temperature Feature Extraction Method for Infrared Images of Electrical Equipment |
| title_sort | optimized otsu segmentation algorithm based temperature feature extraction method for infrared images of electrical equipment |
| topic | power equipment infrared image segmentation temperature feature extraction |
| url | https://www.mdpi.com/1424-8220/24/4/1126 |
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