An Improved CNN VGG19 Architecture for Detection and Classification of Electric Fire Short-Circuit Marks

An Improved CNN VGG19 Architecture for Detection and Classification of Electric Fire Short-Circuit Marks

In this paper, the VGG19 algorithm was used by applying the transfer learning for the classification of molten traces of electric fire arc-beads data which is one of the most used models in convolutional neural network(CNN) computer vision tasks. The most essential basis for detecting direct indications of electric fires is the melting traces of wires that occur at the site of an electric fire, depending on the severity and shape of the melting. The proposed VGG19 method was altered and used such that it could detect molten traces, and the molten trace data of the wires required for learning were created in the lab. The final validation accuracy result was 96.31% with validation loss of 0.1169. Through the result of securing such high accuracy, the possibility of using the melting trace detection algorithm to verify the presence or absence of an electric fire was shown.

In this paper, the VGG19 algorithm was used by applying the transfer learning for the classification of molten traces of electric fire arc-beads data which is one of the most used models in convolutional neural network(CNN) computer vision tasks. The most essential basis for detecting direct indications of electric fires is the melting traces of wires that occur at the site of an electric fire, depending on the severity and shape of the melting. The proposed VGG19 method was altered and used such that it could detect molten traces, and the molten trace data of the wires required for learning were created in the lab. The final validation accuracy result was 96.31% with validation loss of 0.1169. Through the result of securing such high accuracy, the possibility of using the melting trace detection algorithm to verify the presence or absence of an electric fire was shown.