Advances in instance segmentation: Technologies, metrics and applications in computer vision

IF 5.5 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
José M. Molina , Juan P. Llerena , Luis Usero , Miguel A. Patricio
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引用次数: 0

Abstract

Instance segmentation is an advanced technique in computer vision that focuses on identifying and classifying each individual object in an image at the pixel level. Unlike semantic segmentation, which groups pixels of similar objects without distinguishing between different instances, instance segmentation assigns unique labels to each object, even if they are of the same class. This makes it possible not only to detect the presence and category of objects in an image but also to locate each specific instance and clearly distinguish them from each other. This problem not only advances the technical and theoretical understanding of how machines see and process digital images, but also has a direct impact on various industries and sectors where computer vision is an essential part of the system. In this paper, we present the current deep learning-based technologies, the metrics used for their evaluation, and a review of general and concrete datasets in general and drone-specific contexts. The results of this study provide a compendium of easily deployable deep learning-based technologies. This review paper aims to accelerate the process of understanding and using instance segmentation technologies for the reader.
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来源期刊
Neurocomputing
Neurocomputing 工程技术-计算机:人工智能
CiteScore
13.10
自引率
10.00%
发文量
1382
审稿时长
70 days
期刊介绍: Neurocomputing publishes articles describing recent fundamental contributions in the field of neurocomputing. Neurocomputing theory, practice and applications are the essential topics being covered.
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