Survey on semantic segmentation using deep learning techniques

•Extensive survey on deep neural networks for semantic segmentation.•110 neural network models are categorized into 10 different concepts.•Review of state-of-the-art datasets and evaluation metrics for semantic segmentation.•Models analysis based on structural design and their performance on tested datasets.•Pointing out some of the open problems and possible solutions. Semantic segmentation is a challenging task in computer vision systems. A lot of methods have been developed to tackle this problem ranging from autonomous vehicles, human-computer interaction, to robotics, medical research, agriculture and so on. Many of these methods have been built using the deep learning paradigm that has shown a salient performance. For this reason, we propose to survey these methods by, first categorizing them into ten different classes according to the common concepts underlying their architectures. Second, by providing an overview of the publicly available datasets on which they have been assessed. In addition, we present the common evaluation matrix used to measure their accuracy. Moreover, we focus on some of the methods and look closely at their architectures in order to find out how they have achieved their reported performances. Finally, we conclude by discussing some of the open problems and their possible solutions.