Swgan: A new algorithm of adhesive rice image segmentation based on improved generative adversarial networks

Image segmentation is a crucial part in the automatic detection of rice appearance quality. Due to morphological characteristics of rice grains, missed detection and non-smooth boundaries may exist in the image segmentation of adhesive rice. To address the above issues, this study proposes a novel model named Swgan combined generative adversarial networks (GANs) with nested skip connections for obtaining accurate masks. In order to learn the mask distribution of each object in adhesive rice image and further avoid missed detection, the discriminator in GAN is used as a modifier of Cascade Mask R-CNN model to allow the generator to overcome the limitation of mask generation training, namely detecting multiply objects as a single target. Moreover, the Swgan utilizes Swin-Transformer as the backbone network and incorporates the Cascade Mask R-CNN framework and Nested Feature Pyramid Network (Nested-FPN) to maintain the mask's boundary smoothness during forward propagation. Experimental results indicate that the Swgan is used to obtain better segmentation results from objective detection and segmentation under complex conditions of adhesive rice when compared with state-ofart algorithms. Overall, the Swgan with satisfactory accuracy in image segmentation of adhesive rice combined with physical indicators detection provide reliable quality assessment of rice grains.