A review of the current state-of-the-art in semantic segmentation using deep learning techniques.
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Semantic Segmentation: An Introduction
Deep learning has been applied successfully to many computer vision tasks such as image classification, object detection, and face recognition. More recently, semantic segmentation, which aims to assign a label to every pixel in an image, has also benefited from the advances in deep learning. In this review, we first introduce the four most popular types of deep learning network architectures for semantic segmentation: fully convolutional networks (FCN), recurrent neural networks (RNN), encoder-decoder networks, and dilated/atrous convolutional networks. We then briefly survey some of the recent advances in deep learning techniques that have been applied to semantic segmentation. These include 1) new types of layers such as atrous/dilated convolutions, recurrent/dense connections, and Squeeze-and-Excitation (SE) blocks; 2) new loss functions such as focal loss and Jaccard loss; 3) new bottlenecks for faster inference; 4) new data augmentation methods; 5) new strategies for training very deep network architectures; 6) methods for combining multiple semantic segmentation models. Finally, we discuss some common issues and promising directions for future research in semantic segmentation.
Traditional Semantic Segmentation Methods
Semantic segmentation is the process of classifying each pixel in an image. Traditional methods for semantic segmentation are based on hand-crafted features extraction from images or their corresponding pixels. However, these methods usually do not take into account the global context of an image, which is important for understanding the semantical content of an image. Therefore, these methods usually require a large amount of training data to achieve good performance and are often limited to basic tasks such as scene labeling and object recognition.
Deep learning approaches have been shown to be very effective in semantic segmentation thanks to their ability to learn rich representations from data. In this review, we will firstly present some traditional methods for semantic segmentation. Then, we will give an overview of deep learning techniques that have been applied to this task. Finally, we will discuss some future directions and challenges in this field.
Deep Learning for Semantic Segmentation
Semantic segmentation is one of the most fundamental and important tasks in computer vision. It is a pixel-wise classification task that requires a model to predict the class of each pixel in an image. The goal of this review article is to provide the reader with a comprehensive survey of deep learning techniques applied to semantic segmentation. We begin by providing a brief overview of the history of semantic segmentation and its connection to other computer vision tasks. We then survey the most popular deep learning architectures for semantic segmentation, including fully convolutional networks, deconvolutional networks, and recurrent neural networks. We also discuss recent advances in deep learning that have been applied to semantic segmentation, such as object detection, instance segmentation, and panoptic segmentation. Finally, we conclude with a discussion of the future direction of this field.
Fully Convolutional Networks (FCNs) for Semantic Segmentation
Fully Convolutional Networks (FCNs) are a popular deep learning approach for semantic segmentation. FCNs are similar to traditional Convolutional Neural Networks (CNNs), but they are designed to handle inputs of arbitrary size, such as images of full resolution. This makes FCNs well suited for pixel-wise prediction tasks like semantic segmentation.
One key difference between FCNs and CNNs is that FCNs do not have fully connected layers. This means that the output of an FCN is a prediction map over an input image, rather than a 1D vector of classification probabilities. Another key difference is that FCNs typically use upsampling layers to increase the resolution of their predictions.
While CNNs can be used for semantic segmentation, they are not well suited for handling inputs of arbitrary size. This is because CNNs typically have fully connected layers, which expect 1D vectors as input. When applied to 2D images, CNNs first need to convert the image into a 1D vector before they can processed by the fully connected layer. This conversion process can introduce distortions which can make the predictions less accurate.
FCNs avoid this problem by using convolutional layers instead of fully connected layers. This means that FCNs can directly process 2D images, without needing to convert them into 1D vectors first. The output of an FCN is a prediction map over an input image, rather than a 1D vector of classification probabilities.
Another key difference between FCNs and CNNs is that FCNs typically use upsampling layers to increase the resolution of their predictions. Upsampling is a process whereby lower resolution features arecombined to create higher resolution features. For example, if an input image has a resolution of 512×512 pixels, and an upsampling factor of 2, then the output prediction map will have a resolution of 1024×1024 pixels.
Upsampling allows FCNs to make predictions at any desired resolution, making them well suited for pixel-wise tasks like semantic segmentation. In contrast, CNNs typically make predictions at a fixed resolution, which may not be appropriate for all applications.
There are many different types of upsampling layers that can be used in FCNs, such as bilinear interpolation or transposed convolutions. The choice of upsampling layer will depend on the application and the trade-offs between accuracy and computational efficiency that are required
In semantic segmentation, the goal is to classify each pixel in an image into one of a set of predefined categories. Fully convolutional networks (FCNs) are a popular choice for semantic segmentation. FCNs are built by converting a pretrained classification network such as VGG16 or ResNet50 into a fully convolutional network by replacing the Fully Connected (FC) layers with 1×1 convolutional layers. FCNs have been shown to give state-of-the-art results on many semantic segmentation benchmarks such as PASCAL VOC and Cityscapes.
There are many different FCN architectures, each with its own strengths and weaknesses. In this review, we will cover some of the most popular FCN architectures, including FCN-8s, U-Net, and SegNet. We will also discuss some of the latest research on FCN architectures, including DeepLab and Mask R-CNN.
Deep learning has been successfully applied to various problems in the domain of computer vision, such as image classification, detection, and semantic segmentation. In this paper, we focus on the problem of semantic segmentation using fully convolutional neural networks (FCNs). We firstly briefly review the representative FCN-based approaches for semantic segmentation. Then, we present a comprehensive evaluation of FCN training by comprehensively investigating several key factors that may impact FCN training, including (1) batch size; (2) network depth; (3) dataset scale; and (4) initialization strategies. Our experimental results show that: (1) a large batch size is beneficial to FCN training; (2) a shallower network Downloaded from http://www.vision-systems.com is preferable for FCNs; (3) FCNs usually perform better when trained on a large dataset; and (4) the Xavier initialization is the best among various initialization schemes for FCNs. Finally, we discuss several future research directions for applying deep learning to semantic segmentation.
Most fully convolutional architectures for semantic segmentation adopt an encoder-decoder structure. The FCN inference applied to semantic segmentation on this structure is as follows: images are fed into the encoder part of the network which outputs feature maps. These feature maps are then upsampled by the decoder part of the network to outputpixel-wise class predictions.
Semantic Segmentation using FCNs: A Review
Deep learning is a powerful tool for many computer vision tasks, including semantic segmentation. In semantic segmentation, the goal is to label each pixel in an image with its corresponding class. For example, in an image of a cityscape, each pixel would be labeled as part of the background, a building, road, sky, etc.
One popular deep learning approach for semantic segmentation is fully convolutional networks (FCNs). FCNs are similar to standard convolutional neural networks (CNNs), but they are designed to output a full prediction map instead of just a single class label.
There are many different FCN architectures that have been proposed, and each has its own strengths and weaknesses. In this review, we will survey some of the most popular FCN architectures and discuss their pros and cons. We will also touch on some important considerations for training and deploying FCNs.
Other Deep Learning Techniques for Semantic Segmentation
Other deep learning techniques that have also been applied to semantic segmentation problems are support vector machines (SVMs) [32,33], CRFs , and generative models such as RNNs  and LSTMs . However, these methods are not as widely used as the ones previously mentioned and will not be discussed in this review.
Deep learning techniques have shown to be effective in semantic segmentation, with a better performance than traditional methods in most cases. These techniques are able to learn complex models directly from data, without depending on handcrafted features. Different deep learning architectures have been proposed for semantic segmentation, such as fully convolutional networks (FCN), U-Net, SegNet and Mask R-CNN.
Despite the good results achieved by deep learning methods, there are still some challenges to be addressed. First, the computational cost of these methods is high, which limits their use in real-time applications. Second, the performance of deep learning methods is still not as good as human annotation for some classes and tasks. Finally, the generalization ability of deep learning models is still not perfect, which means that they need more data for training.
Keyword: A Review on Deep Learning Techniques Applied to Semantic Segmentation