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 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.