Evaluating the Effectiveness of Panoptic Segmentation Through Comparative Analysis

Abstract

Image segmentation method is extensively used in the fields of computer vision, machine learning, and artificial intelligence. The task of segmentation is to distinguish objects in images either by their boundaries or as entire objects from the entire image. Image segmentation methods are implemented as instance, semantic, and panoptic segmentation. In this article, the panoptic segmentation method, seen as an advanced stage of instance and semantic segmentation, has been applied to three datasets and compared with the instance segmentation method. Experimental results are presented visually. Numerical results have been analyzed with the Panoptic Quality (PQ) and Semantic Quality (SQ) metrics. It has been observed that the segmentation outcome was best for the CityScapes dataset for panoptic segmentation.