Önceden Eğitilmiş CNN Modellerin Gürültülü Ortamlarda Görüntü Sınıflandırması Açısından Değerlendirilmesi

Abstract

This work aims at testing the efficiency of the pre-trained models in terms of classifying images in noisy environments. To this end, we proposed injecting Gaussian noise into the images in the used datasets gradually to see how the performance of that models can be affected by the proportion of the noise in the image. Afterward, three different case studies have been conducted for evaluating the performance of six different well-known pre-trained models namely MobileNet, ResNet, GoogleNet, EfficientNet, VGG19, and Xception. In the first case study, it has been proposed to train these models using a high-quality image dataset and test them using the same datasets after injecting their images with different levels of Gaussian noise. In the second case study, we proposed training the models using the created noisy image datasets in order to investigate how the training process can be affected by the noises in the environment. In the third case study, we proposed using the non-local means algorithm to denoise the images in the noisy datasets and testing the models trained using the original datasets using these de-noised image datasets. To the best of our knowledge, this is the first time that the effects of noise on well-known pre-trained CNN architectures have been comprehensively investigated with this number of considered models. The obtained results showed that while these types of models can work very well in ideal environments their performances can drop down due to the conditions of the working environment, which reflects the need for some auxiliary models that should be used as a pre-processing phase to improve the performance of these models.