Comparative Analysis of Convolutional Neural Networks for Optical Fiber Classification

This paper employs deep learning algorithms to address the classification of optical fiber cables through image processing techniques. The VGG-16 and Res-Net50 neural network architectures are utilized, and their performance is evaluated in comparison. The experiments were developed in an optical fiber dataset composed of optical fiber images and increased with synthetic data using the data augmentation technique; the dataset has optical fiber images in different states of preservation and colors. The neural networks will classify the images into two classes ("good" or "bad").