Panoptic Image Segmentation through Unet combined with Melody Search Optimization Algorithm for the Realistic Scene Image Understanding

Realistic Scene understanding is a challenging task by recognizing instances along with the semantic scene. This work, efficient panoptic image segmentation through a deep UNet combined with the melody search optimization algorithm for realistic scene images understanding. The visually appealing things have a variety of constituent pieces that capture the unintended organisational linkages between the crucial components of the carefully considered objects and, as a result, group them altogether according to a perceptual organisation model. The melody search optimization algorithm reduces the Unet parameters, thereby improving the accuracy, UNet combines semantic and instance segmentation predictions to form panoptic predictions and reduces the computational time and memory resources. Hence, the scene understanding is performed without requiring knowledge of the physically inspiring things in advance. Our approach is evaluated on four challenging benchmarks: Mapillary Vistas, Indian Driving Dataset, KITTI, and cityscapes. The results show that our method performs better than the current state-of-the-art methodologies for comprehending scene photos. The performance of the presented method was assessed utilizing panoptic quality metrics and computational time.