How deep learning is empowering semantic segmentation (original) (raw)
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IJERT-A Survey on Semantic Segmentation using Deep Learning Techniques
International Journal of Engineering Research and Technology (IJERT), 2021
https://www.ijert.org/a-survey-on-semantic-segmentation-using-deep-learning-techniques https://www.ijert.org/research/a-survey-on-semantic-segmentation-using-deep-learning-techniques-IJERTCONV9IS05011.pdf Semantic segmentation is a challenging task in the field of computer vision. It is process of classifying each pixel belonging to a particular label. It has many challenging applications such as autonomous vehicles, human-computer interaction, robot navigation, medical research and so on, which motivates us to survey the different semantic segmentation architectures. Most of these methods have been built using the deep learning techniques. In this paper we made a review of some state-of-the-art Convolutional Neural Network(CNN) architectures such as AlexNet, GoogleNet, VGGNet, ResNet which form the basis for Semantic Segmentation. Further, we presenteddifferent semanticsegmentation architectures such as Fully Convolutional Network (FCN), ParseNet, Deconvolution Network, U-Net, Feature Pyramid Network(FPN), Mask R-CNN. Finally, we compared the performances of all these architectures.
Deep-Learning-Based Approaches for Semantic Segmentation of Natural Scene Images: A Review
Electronics
The task of semantic segmentation holds a fundamental position in the field of computer vision. Assigning a semantic label to each pixel in an image is a challenging task. In recent times, significant advancements have been achieved in the field of semantic segmentation through the application of Convolutional Neural Networks (CNN) techniques based on deep learning. This paper presents a comprehensive and structured analysis of approximately 150 methods of semantic segmentation based on CNN within the last decade. Moreover, it examines 15 well-known datasets in the semantic segmentation field. These datasets consist of 2D and 3D image and video frames, including general, indoor, outdoor, and street scenes. Furthermore, this paper mentions several recent techniques, such as SAM, UDA, and common post-processing algorithms, such as CRF and MRF. Additionally, this paper analyzes the performance evaluation of reviewed state-of-the-art methods, pioneering methods, common backbone networks...
IEEE Access
Machine learning and deep learning algorithms are widely used in computer science domains. These algorithms are mostly used for classification and regression problems in almost every field of life. Semantic segmentation is an instantly growing research topic in the last few decades that refers to the association of each pixel in the image to the class it belongs. This paper illustrates the systematic survey of advanced research in the field of semantic segmentation till date. This study provides the brief knowledge about the latest proposed methods in the domain of semantic segmentation. The proposed study comprehends the concepts, techniques, tool, and results of different research frameworks proposed in the context of semantic segmentation. This study discusses the latest research papers in which machine learning and deep learning techniques are exploited for semantic segmentation and published between 2016 and 2021. The systematic literature review collected from seven different article libraries including ACM digital Library, Google Scholar, IEEE Xplore, Science Direct, Google Books, Refseek and Worldwide Science. For assuring the quality of the paper those papers are selected which have several citations on standardized platforms. Most of the studies used COCO, PASCAL, Cityscapes and CamVid dataset for training and validation of the machine learning and deep learning models. The results of the selected research articles are collected in the form of accuracy, mIoU value, F1 score, precision, and recall. In this study, we also conclude that most of the semantic segmentation studies use ResNet as the backbone of the architecture and none of the researchers used ensemble learning methods for semantic segmentation that is the loophole of the selected studies.
Applying Deep Learning for Image Segmentation: A Survey
World Congress on Electrical Engineering and Computer Systems and Science
Image segmentation is one of the most important branches of image processing. But it comes with various challenges and problems to be solved. Researchers are always working on improving the accuracy, quality and performance of image segmentation techniques. As in modern days, deep learning being involved in almost all problem solving, it is being used in image segmentation too. In this paper, we discussed few image segmentation techniques developed using deep learning, some implementation of these techniques to applications. And lastly, we addressed some limitations, challenges and research scopes for future.
State of the Art Techniques to Advance Deep Networks for Semantic Segmentation
U.Porto Journal of Engineering
In recent times, the computer vision community has seen remarkable growth in the field of scene understanding. With such a wide prevalence of images, the importance of this field is growing rapidly along with the technologies involved in it. Semantic Segmentation is an important step in scene understanding which requires the assignment of each pixel in an image to a pre-defined class and achieving 100% accuracy is a challenging task, thereby making it an active research topic among researchers. In this paper, an extensive study and review of the existing Deep Learning (DL) based techniques used for Semantic Segmentation is carried out along with a summary of the datasets and evaluation metrics used for it. The study involved the meticulous selection of relevant research papers in the field of interest by search based on several defined keywords. The study begins with a general and broader focus on Semantic Segmentation as a problem and further narrows its focus on existing Deep Lear...
Real-Time Semantic Image Segmentation with Deep Learning for Autonomous Driving: A Survey
Applied Sciences
Semantic image segmentation for autonomous driving is a challenging task due to its requirement for both effectiveness and efficiency. Recent developments in deep learning have demonstrated important performance boosting in terms of accuracy. In this paper, we present a comprehensive overview of the state-of-the-art semantic image segmentation methods using deep-learning techniques aiming to operate in real time so that can efficiently support an autonomous driving scenario. To this end, the presented overview puts a particular emphasis on the presentation of all those approaches which permit inference time reduction, while an analysis of the existing methods is addressed by taking into account their end-to-end functionality, as well as a comparative study that relies upon a consistent evaluation framework. Finally, a fruitful discussion is presented that provides key insights for the current trend and future research directions in real-time semantic image segmentation with deep lea...
Nashriyyahʼi farhangʼi Khurāsān, 2023
Semantic segmentation is a branch of computer vision, used extensively in image search engines, automated driving, intelligent agriculture, disaster management, and other machine-human interactions. Semantic segmentation aims to predict a label for each pixel from a given label set, according to semantic information. Among the proposed methods and architectures, researchers have focused on deep learning algorithms due to their good feature learning results. Thus, many studies have explored the structure of deep neural networks, especially convolutional neural networks. Most of the modern semantic segmentation models are based on fully convolutional networks (FCN), which first replace the fully connected layers in common classification networks with convolutional layers, getting pixel-level prediction results. After that, a lot of methods are proposed to improve the basic FCN methods results. With the increasing complexity and variety of existing data structures, more powerful neural networks and the development of existing networks are needed. This study aims to segment a high-resolution (HR) image dataset into six separate classes. Here, an overview of some important deep learning architectures will be presented with a focus on methods producing remarkable scores in segmentation metrics such as accuracy and F1-score. Finally, their segmentation results will be discussed and we would see that the methods, which are superior in the overall accuracy and overall F1-score, are not necessarily the best in all classes. Therefore, the results of this paper lead to the point to choose the segmentation algorithm according to the application of segmentation and the importance degree of each class.
Semantic Segmentation using Fully Convolutional Net: A Review
Semantic segmentation has paved its way in predicting the models using dense pixel-wise prediction method apart from classification. The presented models for semantic partitions the image into semantically meaningful chunks and classifies each chunk into one of the predetermined classes. The presented model reduces the parameters to be trained and helps in up-sampling; it describes quality and accuracy and efficient mechanism. Deeper the layers are, helps in capturing the high-level semantic features from the previous convolutional layers.
A Brief Survey and an Application of Semantic Image Segmentation for Autonomous Driving
Handbook of Deep Learning Applications, 2019
Deep learning is a fast-growing machine learning approach to perceive and understand large amounts of data. In this paper, general information about the deep learning approach which is attracted much attention in the field of machine learning is given in recent years and an application about semantic image segmentation is carried out in order to help autonomous driving of autonomous vehicles. This application is implemented with Fully Convolutional Network (FCN) architectures obtained by modifying the Convolutional Neural Network (CNN) architectures based on deep learning. Experimental studies for the application are utilized 4 different FCN architectures named FCN-AlexNet, FCN-8s, FCN-16s and FCN-32s. For the experimental studies, FCNs are first trained separately and validation accuracies of these trained network models on the used dataset is compared. In addition, image segmentation inferences are visualized to take account of how precisely FCN architectures can segment objects.
Intelligent Semantic Segmentation for Self-Driving Vehicles Using Deep Learning
Computational Intelligence and Neuroscience
Understanding the situation is a critical component of any self-driving system. Accurate real-time visual signal processing to create pixelwise classed pictures, also known as semantic segmentation, is critical for scenario comprehension and subsequent acceptance of this new technology. Due to the intricate interaction between pixels in each frame of the received camera data, such efficiency in terms of processing time and accuracy could not be achieved prior to recent advances in deep learning algorithms. We present an effective approach for semantic segmentation for self-driving automobiles in this study. We combine deep learning architectures like convolutional neural networks and autoencoders, as well as cutting-edge approaches like feature pyramid networks and bottleneck residual blocks, to develop our model. The CamVid dataset, which has undergone considerable data augmentation, is utilised to train and test our model. To validate the suggested model, we compare the acquired f...