Simulating Bruise and Defects on Mango images using Image-to-Image Translation Generative Adversarial Networks

A well-balanced dataset is essential for every computer vision task. However, the process of gathering data from various sources is laborious and time-consuming. The lack of samples and class imbalance will reduce the reliability of the CNN model in image classification and recognition tasks. In this research, we examine the use of Image-to-Image translation with conditional GAN for producing synthetic mango images with bruises. We introduce a conditional GAN for producing mango images with controlled surface defects, which is suited for dataset augmentation tasks within the fruit classification problem domain. The findings shows that our networks is able to generate mango images with bruises that are very close to the ground truth with FID value of 37.0