Hericles Ferraz, Rogério Sales Gonçalves, Breno Batista Moura, Daniel Edgardo Tió Sudbrack, Paulo Victor Trautmann, Bruno Clasen, Rafael Zimmermann Homma, Reinaldo A. C. Bianchi
{"title":"利用深度神经网络对电网高压铁塔绝缘子清洁度进行自动分类","authors":"Hericles Ferraz, Rogério Sales Gonçalves, Breno Batista Moura, Daniel Edgardo Tió Sudbrack, Paulo Victor Trautmann, Bruno Clasen, Rafael Zimmermann Homma, Reinaldo A. C. Bianchi","doi":"10.1007/s41315-024-00349-8","DOIUrl":null,"url":null,"abstract":"<p>String insulators are components in high-voltage towers responsible for preventing energy dissipation through the tower structure; that is, they are responsible for isolating the high voltage in the electrical network cables. These string insulators must be clean for best performance and to avoid malfunctions. Verifying the necessity for cleaning/washing is most often performed by human visual observation, which can lead to interpretation errors, in addition to bringing risks to the physical integrity of humans in the vicinity of these electrical systems. Thus, this paper aims to develop an algorithm to detect and classify these insulators. The proposed algorithm uses artificial intelligence techniques and analyzes the image, inferring the state of cleanliness of the analyzed insulator. For the development of this algorithm, it was necessary to build a synthetic database using CAD software such as Inventor and Unity-3D due to image limitations available from dirty insulator strings. In this paper, two distinct neural networks are built using supervised learning techniques, where the first one is for detecting the chain of insulators, and the second is for detecting the type of dirt on the disk surface. In the first stage, techniques that use supervised learning are studied, more aimed explicitly at semantic segmentation networks, and in the second stage, classification deep neural networks were used to detect the type of impurities. In detecting insulator strings, an average dice coefficient of 0.95 was achieved for simulated images and 0.92 for natural images, with learning parameters based on a database with only simulated images. The average accuracy obtained in the dirt classification stage was 0.98.</p>","PeriodicalId":44563,"journal":{"name":"International Journal of Intelligent Robotics and Applications","volume":null,"pages":null},"PeriodicalIF":2.1000,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Automated classification of electrical network high-voltage tower insulator cleanliness using deep neural networks\",\"authors\":\"Hericles Ferraz, Rogério Sales Gonçalves, Breno Batista Moura, Daniel Edgardo Tió Sudbrack, Paulo Victor Trautmann, Bruno Clasen, Rafael Zimmermann Homma, Reinaldo A. C. Bianchi\",\"doi\":\"10.1007/s41315-024-00349-8\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>String insulators are components in high-voltage towers responsible for preventing energy dissipation through the tower structure; that is, they are responsible for isolating the high voltage in the electrical network cables. These string insulators must be clean for best performance and to avoid malfunctions. Verifying the necessity for cleaning/washing is most often performed by human visual observation, which can lead to interpretation errors, in addition to bringing risks to the physical integrity of humans in the vicinity of these electrical systems. Thus, this paper aims to develop an algorithm to detect and classify these insulators. The proposed algorithm uses artificial intelligence techniques and analyzes the image, inferring the state of cleanliness of the analyzed insulator. For the development of this algorithm, it was necessary to build a synthetic database using CAD software such as Inventor and Unity-3D due to image limitations available from dirty insulator strings. In this paper, two distinct neural networks are built using supervised learning techniques, where the first one is for detecting the chain of insulators, and the second is for detecting the type of dirt on the disk surface. In the first stage, techniques that use supervised learning are studied, more aimed explicitly at semantic segmentation networks, and in the second stage, classification deep neural networks were used to detect the type of impurities. In detecting insulator strings, an average dice coefficient of 0.95 was achieved for simulated images and 0.92 for natural images, with learning parameters based on a database with only simulated images. 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Automated classification of electrical network high-voltage tower insulator cleanliness using deep neural networks
String insulators are components in high-voltage towers responsible for preventing energy dissipation through the tower structure; that is, they are responsible for isolating the high voltage in the electrical network cables. These string insulators must be clean for best performance and to avoid malfunctions. Verifying the necessity for cleaning/washing is most often performed by human visual observation, which can lead to interpretation errors, in addition to bringing risks to the physical integrity of humans in the vicinity of these electrical systems. Thus, this paper aims to develop an algorithm to detect and classify these insulators. The proposed algorithm uses artificial intelligence techniques and analyzes the image, inferring the state of cleanliness of the analyzed insulator. For the development of this algorithm, it was necessary to build a synthetic database using CAD software such as Inventor and Unity-3D due to image limitations available from dirty insulator strings. In this paper, two distinct neural networks are built using supervised learning techniques, where the first one is for detecting the chain of insulators, and the second is for detecting the type of dirt on the disk surface. In the first stage, techniques that use supervised learning are studied, more aimed explicitly at semantic segmentation networks, and in the second stage, classification deep neural networks were used to detect the type of impurities. In detecting insulator strings, an average dice coefficient of 0.95 was achieved for simulated images and 0.92 for natural images, with learning parameters based on a database with only simulated images. The average accuracy obtained in the dirt classification stage was 0.98.
期刊介绍:
The International Journal of Intelligent Robotics and Applications (IJIRA) fosters the dissemination of new discoveries and novel technologies that advance developments in robotics and their broad applications. This journal provides a publication and communication platform for all robotics topics, from the theoretical fundamentals and technological advances to various applications including manufacturing, space vehicles, biomedical systems and automobiles, data-storage devices, healthcare systems, home appliances, and intelligent highways. IJIRA welcomes contributions from researchers, professionals and industrial practitioners. It publishes original, high-quality and previously unpublished research papers, brief reports, and critical reviews. Specific areas of interest include, but are not limited to:Advanced actuators and sensorsCollective and social robots Computing, communication and controlDesign, modeling and prototypingHuman and robot interactionMachine learning and intelligenceMobile robots and intelligent autonomous systemsMulti-sensor fusion and perceptionPlanning, navigation and localizationRobot intelligence, learning and linguisticsRobotic vision, recognition and reconstructionBio-mechatronics and roboticsCloud and Swarm roboticsCognitive and neuro roboticsExploration and security roboticsHealthcare, medical and assistive roboticsRobotics for intelligent manufacturingService, social and entertainment roboticsSpace and underwater robotsNovel and emerging applications