FCNs, or Fully Convolutional Networks, are a form of architecture that is primarily used for semantic segmentation. Convolution, pooling, and upsampling are the only locally linked layers they use. Since dense layers aren’t used, there are fewer parameters (making the networks faster to train). It also means that an FCN can handle a wide range of image sizes since all connections are local. In this article, we discussed FCN, semantic segmentation, convolutional layer, and semantic segmentation deep learning.
- Categorization of Computer Vision tasks
- Various Applications of Semantic Segmentation
1) Categorization of Computer Vision tasks
- A typical classification problem entails identifying (and/or localizing) an object in an input image. In such problems, we normally have a single point of interest, so the output is a vector of probabilities for different groups in the training corpus. The topic is assigned to the class mark with the highest probability value.
- Object recognition is a computer vision activity that recognizes and locates objects inside an image or video input. Object detection, in particular, creates bounding boxes around detected objects, allowing us to see where they are in (and how they pass through) a scene.
- The aim of a semantic segmentation task, also known as dense prediction, is to mark each pixel of the input image with the class that represents a particular entity or body. Where the spatial information of a subject and how it interacts with it is relevant, such as for an autonomous vehicle, segmentation is used.
- Instance Segmentation: Object Instance Segmentation is a step forward from semantic segmentation in that it attempts to separate different instances from a single class. It can be thought of as a cross between object detection and semantic segmentation.
2) Various Applications of Semantic Segmentation
- Semantic Segmentation problems can also be known as classification problems, in which each pixel is assigned to one of several object classes. Land use mapping for satellite imagery thus has a use case. Land cover data is useful for a variety of purposes, including tracking deforestation and urbanization areas. Land cover classification is a multi-class semantic segmentation task that determines land cover for each pixel on a satellite image. For traffic management, city planning, and road surveillance, identifying roads and buildings is also a key research subject.
- Autonomous driving is a difficult robotics challenge that necessitates awareness, preparation, and execution in continuously changing environments. Since protection is paramount, this mission must also be completed with the utmost precision. Semantic Segmentation can detect lane markers and traffic signals and provide information about free space on the route.
- Facial segmentation usually includes groups such as skin, hair, eyes, nose, mouth, and context in semantic segmentation. Face segmentation is useful in various computer vision applications, including estimating gender, speech, age, and ethnicity. The face segmentation dataset and model formation is affected by lighting conditions, facial gestures, face orientation, occlusion, and image resolution.
- Fashion – Categorizing clothing items: Due to a large number of groups, clothing parsing is a rather complex activity compared to others. Fine-grained clothing categorization differs from general object or scene segmentation problems in that it necessitates higher-level judgment based on clothing grammar, individual pose variability, and the potentially large number of groups. Clothing parsing has received a lot of attention in the vision community due to its importance in real-world applications, such as e-commerce. Fashionista and CFPD datasets, for example, provide open access to semantic segmentation for clothing products.
- Precision farming robots can minimize the number of herbicides that need to be sprayed in the fields, and semantic segmentation of crops and weeds can help them cause weeding behavior in real-time. Agriculture may benefit from advanced image vision techniques that reduce the need for manual monitoring.
The term “completely convolutional” refers to a neural network that is made up entirely of convolutional layers, with no fully connected layers at the top. A CNN with completely linked layers is just as easy to learn from start to finish as one with fully convolutional layers. The key distinction is that the completely convolutional net is constantly learning filters. Even the network’s final decision-making layers are filters. A completely convolutional neural network attempts to learn representations and make decisions based on local spatial data. The addition of a completely connected layer allows the network to learn everything using global knowledge without regard for the spatial arrangement of the input.
A Completely Convolutional Neural Network (FCN) is a standard CNN with another convolution layer with a broad “receptive region” in place of the last fully connected layer. The goal is to capture the scene’s overall context (Tell us what we have in the image and also give some very rough ideas of the locations of things). If we look at where we have more activations, we obtain some kind of localization when we convert our last fully connected (FC) layer to a convolutional layer. The idea is that if we make our new last Conv layer large enough, the localization effect will be scaled up to the size of our input picture.
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