Generative Adversarial Networks and Their Variants for Precise Fruit Recognition

This research study carefully compares various generative adversarial networks (GANs) and their variations in object detection, focusing on fruit recognition in image datasets. The study assesses the efficacy of InfoGAN, CycleGAN, ConditionalGAN, and StyleGAN in enhancing object detection accuracy. To improve object detection models, test each version of GAN by creating fake images. The process is achieved by conducting a lot of experiments with different fruit datasets that include both common and rare varieties. In addition to traditional evaluation metrics like peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM), this research introduces a novel approach. The study finds the GAN variant that makes synthetic images work best for finding objects by making a link between PSNR and SSIM values and how well they work for finding objects. The findings offer valuable insights into the relationship between GAN-generated image quality and object detection efficacy, highlighting each GAN variant's strengths and weaknesses in fruit recognition. This comparative analysis serves as a foundation for advancing computer vision systems for object detection tasks and provides direction for future research in this field.