Affiliation:
1. School of Automation, Chengdu University of Information Technology, Chengdu 610225, China
2. Key Laboratory of Natural Disaster Monitoring & Early Warning and Assessment of Jiangxi Province, Jiangxi Normal University, Nanchang 330022, China
3. School of Network & Communication Engineering, Chengdu Technological University, Chengdu 610031, China
Abstract
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%.
Funder
Sichuan Science and Technology Program
School Project of Chengdu University of Information Technology
Chengdu Technical Innovation Research Program
International Joint Research Center of Robots and Intelligence Program
Ministry of Education industry-school cooperative education project
Opening Fund of Key Laboratory of Natural Disaster Monitoring, Early Warning and Assessment of Jiangxi Province
Program of Chengdu Technological University
Chengdu Qingyang District science and technology plan project
Subject
Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry
Cited by
5 articles.
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