A Small Object Detection Method for Oil Leakage Defects in Substations Based on Improved Faster-RCNN
Since substations are key parts of power transmission, ensuring the safety of substations involves monitoring whether the substation equipment is in a normal state. Oil leakage detection is one of the necessary daily tasks of substation inspection robots, which can immediately find out whether there...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-08-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/17/7390 |
| _version_ | 1827727791815131136 |
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| author | Qiang Yang Song Ma Dequan Guo Ping Wang Meichen Lin Yangheng Hu |
| author_facet | Qiang Yang Song Ma Dequan Guo Ping Wang Meichen Lin Yangheng Hu |
| author_sort | Qiang Yang |
| collection | DOAJ |
| description | Since substations are key parts of power transmission, ensuring the safety of substations involves monitoring whether the substation equipment is in a normal state. Oil leakage detection is one of the necessary daily tasks of substation inspection robots, which can immediately find out whether there is oil leakage in the equipment in operation so as to ensure the service life of the equipment and maintain the safe and stable operation of the system. At present, there are still some challenges in oil leakage detection in substation equipment: there is a lack of a more accurate method of detecting oil leakage in small objects, and there is no combination of intelligent inspection robots to assist substation inspection workers in judging oil leakage accidents. To address these issues, this paper proposes a small object detection method for oil leakage defects in substations. This paper proposes a small object detection method for oil leakage defects in substations, which is based on the feature extraction network Resnet-101 of the Faster-RCNN model for improvement. In order to decrease the loss of information in the original image, especially for small objects, this method is developed by canceling the downsampling operation and replacing the large convolutional kernel with a small convolutional kernel. In addition, the method proposed in this paper is combined with an intelligent inspection robot, and an oil leakage decision-making scheme is designed, which can provide substation equipment oil leakage maintenance recommendations for substation workers to deal with oil leakage accidents. Finally, the experimental validation of real substation oil leakage image collection is carried out by the intelligent inspection robot equipped with a camera. The experimental results show that the proposed FRRNet101-c model in this paper has the best performance for oil leakage detection in substation equipment compared with several baseline models, improving the Mean Average Precision (mAP) by 6.3%, especially in detecting small objects, which has improved by 12%. |
| first_indexed | 2024-03-10T23:13:01Z |
| format | Article |
| id | doaj.art-0b397227e0e34da193a8cb2abd24bb33 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T23:13:01Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-0b397227e0e34da193a8cb2abd24bb332023-11-19T08:49:12ZengMDPI AGSensors1424-82202023-08-012317739010.3390/s23177390A Small Object Detection Method for Oil Leakage Defects in Substations Based on Improved Faster-RCNNQiang Yang0Song Ma1Dequan Guo2Ping Wang3Meichen Lin4Yangheng Hu5School of Automation, Chengdu University of Information Technology, Chengdu 610225, ChinaSchool of Automation, Chengdu University of Information Technology, Chengdu 610225, ChinaSchool of Automation, Chengdu University of Information Technology, Chengdu 610225, ChinaSchool of Network & Communication Engineering, Chengdu Technological University, Chengdu 610031, ChinaSchool of Automation, Chengdu University of Information Technology, Chengdu 610225, ChinaSchool of Automation, Chengdu University of Information Technology, Chengdu 610225, ChinaSince substations are key parts of power transmission, ensuring the safety of substations involves monitoring whether the substation equipment is in a normal state. Oil leakage detection is one of the necessary daily tasks of substation inspection robots, which can immediately find out whether there is oil leakage in the equipment in operation so as to ensure the service life of the equipment and maintain the safe and stable operation of the system. At present, there are still some challenges in oil leakage detection in substation equipment: there is a lack of a more accurate method of detecting oil leakage in small objects, and there is no combination of intelligent inspection robots to assist substation inspection workers in judging oil leakage accidents. To address these issues, this paper proposes a small object detection method for oil leakage defects in substations. This paper proposes a small object detection method for oil leakage defects in substations, which is based on the feature extraction network Resnet-101 of the Faster-RCNN model for improvement. In order to decrease the loss of information in the original image, especially for small objects, this method is developed by canceling the downsampling operation and replacing the large convolutional kernel with a small convolutional kernel. In addition, the method proposed in this paper is combined with an intelligent inspection robot, and an oil leakage decision-making scheme is designed, which can provide substation equipment oil leakage maintenance recommendations for substation workers to deal with oil leakage accidents. Finally, the experimental validation of real substation oil leakage image collection is carried out by the intelligent inspection robot equipped with a camera. The experimental results show that the proposed FRRNet101-c model in this paper has the best performance for oil leakage detection in substation equipment compared with several baseline models, improving the Mean Average Precision (mAP) by 6.3%, especially in detecting small objects, which has improved by 12%.https://www.mdpi.com/1424-8220/23/17/7390oil leakage detectionsmall object detectionsubstation equipmentsfaster-RCNNintelligent inspection robot |
| spellingShingle | Qiang Yang Song Ma Dequan Guo Ping Wang Meichen Lin Yangheng Hu A Small Object Detection Method for Oil Leakage Defects in Substations Based on Improved Faster-RCNN Sensors oil leakage detection small object detection substation equipments faster-RCNN intelligent inspection robot |
| title | A Small Object Detection Method for Oil Leakage Defects in Substations Based on Improved Faster-RCNN |
| title_full | A Small Object Detection Method for Oil Leakage Defects in Substations Based on Improved Faster-RCNN |
| title_fullStr | A Small Object Detection Method for Oil Leakage Defects in Substations Based on Improved Faster-RCNN |
| title_full_unstemmed | A Small Object Detection Method for Oil Leakage Defects in Substations Based on Improved Faster-RCNN |
| title_short | A Small Object Detection Method for Oil Leakage Defects in Substations Based on Improved Faster-RCNN |
| title_sort | small object detection method for oil leakage defects in substations based on improved faster rcnn |
| topic | oil leakage detection small object detection substation equipments faster-RCNN intelligent inspection robot |
| url | https://www.mdpi.com/1424-8220/23/17/7390 |
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