Araştırma

Abstract

In this study, performance analysis of generative adversarial network architectures that transform from image to image is made and its performance in synthetic image generation is evaluated. For a quality performance evaluation of these models, the denim2bıyık dataset collected from the real-world area was used instead of standardized datasets. Mustache patterns drawn on denim fabrics are created with a laser device. For this device to create the desired mustache pattern, it is necessary to work with visual editing programs for approximately 2-3 hours by specialized personnel. With the proposed approach, an automatic mustache production process will be realized, errors and time losses in manual production will be eliminated. As a result of our literature research, there is a no different study on the production of denim product images with productive networks. This situation increases the academic original value of the study. GAN architectures used in the study are Pix2Pix, CycleGAN, DiscoGAN, and AttentionGAN. Mustache pattern production performance evaluation and cost analysis were performed on the training and test data in the denim2bıyık dataset of each architecture. As a result of the studies, it is seen that the production speed of the mustache pattern image drops below one second, while the production accuracy reaches 86%.