A Semantically Annotated 15-Class Ground Truth Dataset for Substation Equipment to Train Semantic Segmentation Models
The lack of annotated semantic segmentation datasets for electrical substations in the literature poses a significant problem for machine learning tasks; before training a model, a dataset is needed. This paper presents a new dataset of electric substations with 1660 images annotated with 15 classes...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-07-01
|
| Series: | Data |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2306-5729/8/7/118 |
| _version_ | 1797589636484169728 |
|---|---|
| author | Andreas Anael Pereira Gomes Francisco Itamarati Secolo Ganacim Fabiano Gustavo Silveira Magrin Nara Bobko Leonardo Göbel Fernandes Anselmo Pombeiro Eduardo Félix Ribeiro Romaneli |
| author_facet | Andreas Anael Pereira Gomes Francisco Itamarati Secolo Ganacim Fabiano Gustavo Silveira Magrin Nara Bobko Leonardo Göbel Fernandes Anselmo Pombeiro Eduardo Félix Ribeiro Romaneli |
| author_sort | Andreas Anael Pereira Gomes |
| collection | DOAJ |
| description | The lack of annotated semantic segmentation datasets for electrical substations in the literature poses a significant problem for machine learning tasks; before training a model, a dataset is needed. This paper presents a new dataset of electric substations with 1660 images annotated with 15 classes, including insulators, disconnect switches, transformers and other equipment commonly found in substation environments. The images were captured using a combination of human, fixed and AGV-mounted cameras at different times of the day, providing a diverse set of training and testing data for algorithm development. In total, 50,705 annotations were created by a team of experienced annotators, using a standardized process to ensure accuracy across the dataset. The resulting dataset provides a valuable resource for researchers and practitioners working in the fields of substation automation, substation monitoring and computer vision. Its availability has the potential to advance the state of the art in this important area. |
| first_indexed | 2024-03-11T01:09:24Z |
| format | Article |
| id | doaj.art-18ae96f904b245da9a7ec08fdb21b263 |
| institution | Directory Open Access Journal |
| issn | 2306-5729 |
| language | English |
| last_indexed | 2024-03-11T01:09:24Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Data |
| spelling | doaj.art-18ae96f904b245da9a7ec08fdb21b2632023-11-18T18:56:27ZengMDPI AGData2306-57292023-07-018711810.3390/data8070118A Semantically Annotated 15-Class Ground Truth Dataset for Substation Equipment to Train Semantic Segmentation ModelsAndreas Anael Pereira Gomes0Francisco Itamarati Secolo Ganacim1Fabiano Gustavo Silveira Magrin2Nara Bobko3Leonardo Göbel Fernandes4Anselmo Pombeiro5Eduardo Félix Ribeiro Romaneli6Graduate Program in Energy Systems, Universidade Tecnológica Federal do Paraná (UTFPR), 3165 Sete de Setembro Ave, Curitiba 80230-901, PR, BrazilTecgraf Institute of Technical-Scientific Software Development of PUC-Rio, Pontifícia Universidade Católica do Rio de Janeiro (PUC-Rio), 225 Marquês de São Vicente St, Building Pe. Belisário Velloso, Rio de Janeiro 22453-900, RJ, BrazilGraduate Program in Energy Systems, Universidade Tecnológica Federal do Paraná (UTFPR), 3165 Sete de Setembro Ave, Curitiba 80230-901, PR, BrazilProfessional Master’s Degree in Mathematics in National Network, Universidade Tecnológica Federal do Paraná (UTFPR), 3165 Sete de Setembro Ave, Curitiba 80230-901, PR, BrazilGraduate Program in Electrical and Computer Engineering, Universidade Tecnológica Federal do Paraná (UTFPR), 3165 Sete de Setembro Ave, Curitiba 80230-901, PR, BrazilOperation and Maintenance Engineering Superintendence, Copel, 158 José Izidoro Biazetto St, Curitiba 81200-240, PR, BrazilGraduate Program in Energy Systems, Universidade Tecnológica Federal do Paraná (UTFPR), 3165 Sete de Setembro Ave, Curitiba 80230-901, PR, BrazilThe lack of annotated semantic segmentation datasets for electrical substations in the literature poses a significant problem for machine learning tasks; before training a model, a dataset is needed. This paper presents a new dataset of electric substations with 1660 images annotated with 15 classes, including insulators, disconnect switches, transformers and other equipment commonly found in substation environments. The images were captured using a combination of human, fixed and AGV-mounted cameras at different times of the day, providing a diverse set of training and testing data for algorithm development. In total, 50,705 annotations were created by a team of experienced annotators, using a standardized process to ensure accuracy across the dataset. The resulting dataset provides a valuable resource for researchers and practitioners working in the fields of substation automation, substation monitoring and computer vision. Its availability has the potential to advance the state of the art in this important area.https://www.mdpi.com/2306-5729/8/7/118semantic segmentationannotationlabelingautomated methodssubstation automationmachine learning |
| spellingShingle | Andreas Anael Pereira Gomes Francisco Itamarati Secolo Ganacim Fabiano Gustavo Silveira Magrin Nara Bobko Leonardo Göbel Fernandes Anselmo Pombeiro Eduardo Félix Ribeiro Romaneli A Semantically Annotated 15-Class Ground Truth Dataset for Substation Equipment to Train Semantic Segmentation Models Data semantic segmentation annotation labeling automated methods substation automation machine learning |
| title | A Semantically Annotated 15-Class Ground Truth Dataset for Substation Equipment to Train Semantic Segmentation Models |
| title_full | A Semantically Annotated 15-Class Ground Truth Dataset for Substation Equipment to Train Semantic Segmentation Models |
| title_fullStr | A Semantically Annotated 15-Class Ground Truth Dataset for Substation Equipment to Train Semantic Segmentation Models |
| title_full_unstemmed | A Semantically Annotated 15-Class Ground Truth Dataset for Substation Equipment to Train Semantic Segmentation Models |
| title_short | A Semantically Annotated 15-Class Ground Truth Dataset for Substation Equipment to Train Semantic Segmentation Models |
| title_sort | semantically annotated 15 class ground truth dataset for substation equipment to train semantic segmentation models |
| topic | semantic segmentation annotation labeling automated methods substation automation machine learning |
| url | https://www.mdpi.com/2306-5729/8/7/118 |
| work_keys_str_mv | AT andreasanaelpereiragomes asemanticallyannotated15classgroundtruthdatasetforsubstationequipmenttotrainsemanticsegmentationmodels AT franciscoitamaratisecologanacim asemanticallyannotated15classgroundtruthdatasetforsubstationequipmenttotrainsemanticsegmentationmodels AT fabianogustavosilveiramagrin asemanticallyannotated15classgroundtruthdatasetforsubstationequipmenttotrainsemanticsegmentationmodels AT narabobko asemanticallyannotated15classgroundtruthdatasetforsubstationequipmenttotrainsemanticsegmentationmodels AT leonardogobelfernandes asemanticallyannotated15classgroundtruthdatasetforsubstationequipmenttotrainsemanticsegmentationmodels AT anselmopombeiro asemanticallyannotated15classgroundtruthdatasetforsubstationequipmenttotrainsemanticsegmentationmodels AT eduardofelixribeiroromaneli asemanticallyannotated15classgroundtruthdatasetforsubstationequipmenttotrainsemanticsegmentationmodels AT andreasanaelpereiragomes semanticallyannotated15classgroundtruthdatasetforsubstationequipmenttotrainsemanticsegmentationmodels AT franciscoitamaratisecologanacim semanticallyannotated15classgroundtruthdatasetforsubstationequipmenttotrainsemanticsegmentationmodels AT fabianogustavosilveiramagrin semanticallyannotated15classgroundtruthdatasetforsubstationequipmenttotrainsemanticsegmentationmodels AT narabobko semanticallyannotated15classgroundtruthdatasetforsubstationequipmenttotrainsemanticsegmentationmodels AT leonardogobelfernandes semanticallyannotated15classgroundtruthdatasetforsubstationequipmenttotrainsemanticsegmentationmodels AT anselmopombeiro semanticallyannotated15classgroundtruthdatasetforsubstationequipmenttotrainsemanticsegmentationmodels AT eduardofelixribeiroromaneli semanticallyannotated15classgroundtruthdatasetforsubstationequipmenttotrainsemanticsegmentationmodels |