Classification of Short Circuit Marks in Electric Fire Case with Transfer Learning and Fine-Tuning the Convolutional Neural Network Models

One of the most essential substances for detecting electric fire is electric fire short-circuit marks. The traces of which can be found before and after the electric fire as the short circuit occurs. There are different kinds of electric fire short circuit marks, for instance, grounded, primary, and secondary molten marks these are categorized into the different types of short-circuit marks primary short circuit marks appear before the electric fire occurrence, and secondary short circuit marks appear after an electric fire to identify and classify them is crucial and time-consuming steps and procedures are needed for that purpose in this study we have used five convolutional neural network models such as VGG16, VGG19, Xception, InceptionV3, and Resnet50 to classify the short-circuit marks image data. Furthermore, according to our experiment on dataset among these five models, the best result was of VGG16 because the model performed well without any overfitting problems when we trained the sets of electric fire short circuit image data by applying the data augmentation, transfer learning, and fine-tuning techniques. The validation accuracy result of the VGG16 model at 50 epochs was 92.7% with a validation loss rate of 0.2.