Automated Monitoring of Construction Sites of Electric Power Substations Using Deep Learning
With each passing year, the consumption of electric energy in Brazil and the world increases, making it necessary to adopt measures such as the construction of new plants and the installation of power distribution structures. Monitoring for construction management in companies is still done in perso...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9335576/ |
| _version_ | 1830083737246760960 |
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| author | Bruno Alberto Soares Oliveira Abilio Pereira De Faria Neto Roberto Marcio Arruda Fernandino Rogerio Fernandes Carvalho Amanda Lopes Fernandes Frederico Gadelha Guimaraes |
| author_facet | Bruno Alberto Soares Oliveira Abilio Pereira De Faria Neto Roberto Marcio Arruda Fernandino Rogerio Fernandes Carvalho Amanda Lopes Fernandes Frederico Gadelha Guimaraes |
| author_sort | Bruno Alberto Soares Oliveira |
| collection | DOAJ |
| description | With each passing year, the consumption of electric energy in Brazil and the world increases, making it necessary to adopt measures such as the construction of new plants and the installation of power distribution structures. Monitoring for construction management in companies is still done in person and manually, resulting in expenses that could be avoided. That said, there are opportunities to automate such processes using artificial intelligence and, therefore, the main objective of this work is the development of an automated constructions management system, whose goal is to increase the management and monitoring of substation constructions with the remote monitoring. The system incorporates resources of deep learning to classify the components in bays, comparing the data generated in this recognition with the engineering projects to verify the progress of the installation of these components and generating indicators of conformity and evolution of the construction. To achieve the main objective, a comparison was made among four convolutional neural network architectures: DenseNet, Inception, ResNet, and SqueezeNet, in the classification task. The models were trained with thousands of images extracted from photos of different bays captured in the field and, additionally, data augmentation techniques were applied. The models were trained using transfer learning and fine tuning starting from pre-trained weights in the ImageNet data set. All models obtained results close to 100% in the images of the test set, hence it is possible to conclude that, for the proposed problem, there was no significant difference between the assertiveness of the architectures. The chosen model was part of the final application that monitors the construction management of the bays in the electricity substations. |
| first_indexed | 2024-12-14T16:27:14Z |
| format | Article |
| id | doaj.art-1a525806f22b4175a12b868c1f4724ad |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T16:27:14Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-1a525806f22b4175a12b868c1f4724ad2022-12-21T22:54:39ZengIEEEIEEE Access2169-35362021-01-019191951920710.1109/ACCESS.2021.30544689335576Automated Monitoring of Construction Sites of Electric Power Substations Using Deep LearningBruno Alberto Soares Oliveira0https://orcid.org/0000-0002-1816-7146Abilio Pereira De Faria Neto1Roberto Marcio Arruda Fernandino2https://orcid.org/0000-0003-3173-6558Rogerio Fernandes Carvalho3https://orcid.org/0000-0001-8705-9467Amanda Lopes Fernandes4https://orcid.org/0000-0002-5593-0681Frederico Gadelha Guimaraes5https://orcid.org/0000-0001-9238-8839Graduate Program in Electrical Engineering, Universidade Federal de Minas Gerais, Belo Horizonte, BrazilSVA Tech, Belo Horizonte, BrazilSVA Tech, Belo Horizonte, BrazilSVA Tech, Belo Horizonte, BrazilCPFL Energia, Campinas, BrazilGraduate Program in Electrical Engineering, Universidade Federal de Minas Gerais, Belo Horizonte, BrazilWith each passing year, the consumption of electric energy in Brazil and the world increases, making it necessary to adopt measures such as the construction of new plants and the installation of power distribution structures. Monitoring for construction management in companies is still done in person and manually, resulting in expenses that could be avoided. That said, there are opportunities to automate such processes using artificial intelligence and, therefore, the main objective of this work is the development of an automated constructions management system, whose goal is to increase the management and monitoring of substation constructions with the remote monitoring. The system incorporates resources of deep learning to classify the components in bays, comparing the data generated in this recognition with the engineering projects to verify the progress of the installation of these components and generating indicators of conformity and evolution of the construction. To achieve the main objective, a comparison was made among four convolutional neural network architectures: DenseNet, Inception, ResNet, and SqueezeNet, in the classification task. The models were trained with thousands of images extracted from photos of different bays captured in the field and, additionally, data augmentation techniques were applied. The models were trained using transfer learning and fine tuning starting from pre-trained weights in the ImageNet data set. All models obtained results close to 100% in the images of the test set, hence it is possible to conclude that, for the proposed problem, there was no significant difference between the assertiveness of the architectures. The chosen model was part of the final application that monitors the construction management of the bays in the electricity substations.https://ieeexplore.ieee.org/document/9335576/Computer visioncomputerized monitoringconstruction managementimage classificationmachine learning |
| spellingShingle | Bruno Alberto Soares Oliveira Abilio Pereira De Faria Neto Roberto Marcio Arruda Fernandino Rogerio Fernandes Carvalho Amanda Lopes Fernandes Frederico Gadelha Guimaraes Automated Monitoring of Construction Sites of Electric Power Substations Using Deep Learning IEEE Access Computer vision computerized monitoring construction management image classification machine learning |
| title | Automated Monitoring of Construction Sites of Electric Power Substations Using Deep Learning |
| title_full | Automated Monitoring of Construction Sites of Electric Power Substations Using Deep Learning |
| title_fullStr | Automated Monitoring of Construction Sites of Electric Power Substations Using Deep Learning |
| title_full_unstemmed | Automated Monitoring of Construction Sites of Electric Power Substations Using Deep Learning |
| title_short | Automated Monitoring of Construction Sites of Electric Power Substations Using Deep Learning |
| title_sort | automated monitoring of construction sites of electric power substations using deep learning |
| topic | Computer vision computerized monitoring construction management image classification machine learning |
| url | https://ieeexplore.ieee.org/document/9335576/ |
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