Evolution of Image Segmentation using Deep Convolutional Neural Network: A Survey
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From the autonomous car driving to medical diagnosis, the requirement of the task of image segmentation is everywhere. Segmentation of an image is one of the indispensable tasks in computer vision. This task is comparatively complicated than other vision tasks as it needs low level spatial information. Basically, image segmentation can be of two types: semantic segmentation and instance segmentation. The combined version of these two basic tasks is known as panoptic segmentation. In the recent era, the success of deep convolutional neural network (CNN) has influenced the field of segmentation greatly and gave us various successful models till date. In this survey, we are going to take a glance on the evolution of both semantic and instance segmentation work based on CNN. We have also specified architectural details of some state-of-the-art models and discuss their comparative training details to present a lucid understanding of hyper-parameter tuning of those models. We have also drawn a comparison among the performance of those models on different datasets.
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