A Systematic Review of Deep Learning Techniques for Semantic Image Segmentation: Methods, Future Directions, and Challenges

Abstract

The advancements in the methods and techniques in the field of computer vision have enabled numerous applications based on understanding and analysis of image data. Moreover, deep learning has brought a massive shift in image analysis, thereby attracting the attention of researchers worldwide. Many real-life application areas used the image segmentation techniques for the identification of different regions in an image and classify them into clusters depending upon the similarity. Many conventional techniques, namely thresh-olding, k-means clustering histogram-based segmentation, and edge detection algorithms were applied for the segmentation; these pre-existing methods were found to be less efficient because of human intervention. But with the turn-up of deep learning, it is considered as a predominant method in image processing. 50In today’s era where computer vision is contemplating image segmentation for various applications, it is of utmost importance to have a detailed review of it. In the past decade, image segmentation has evolved a lot and is defined on two levels of granularity, namely semantic segmentation and instance segmentation. Furthermore, semantic segmentation segments unknown objects or new objects and classify pixels which are semantically together. This approach can lay down the foundations for new models to improve prior existing computer vision methods. It is entrenched as a vigorous implement for the critical analysis of the different areas in given images. Firstly, we elucidate the basic terms and some mandatory concepts related to this particular field for a better understanding of the naive. Then, the chapter focuses on different methods and network structures which are applied in semantic image segmentation for deep analysis of images in different applications in contrast to conventional approaches. Also, we outline the strengths and weaknesses of this approach to present a superior perspective to the individuals. At last, we strive to disclose challenges of semantic image segmentation processes concurrence with deep learning and point out a set of promising future works.