the most complete guide 2024
What do autonomous cars, medical diagnostic systems and satellite images of the Earth have in common?
The answer is simple: they all depend on the ability of machines to “see” and understand the world around them. In order for a computer to recognize objects in an image and distinguish the sky from a road, a person from a car, or a forest from a building, it is necessary to use image segmentation technologies. But how exactly do machines learn this vision? Here we must remember about semantic segmentation.
When and why is semantic segmentation used?
In a nutshell, this is one of the key tasks in the field of computer vision, it helps machines distinguish between different classes of objects and background regions in an image.
For semantic classification, we select each pixel of the image, and each image segment is associated with a specific class. For example, in a photo of a cityscape, the model highlights buildings, roads, trees and sky, assigning each pixel to its own class. This helps the machine “see” an image the way a human does, identifying individual objects and areas. As a result, the machine recognizes important contexts in digital images: landscapes, photographs of people, animals.
In general, image segmentation problems fall into three main groups:
Semantic segmentation: This type of segmentation classifies each pixel in an image, but does not distinguish between different instances of the same object. For example, all cars in the image will be assigned to the same category “car”, without dividing into individual instances.
Instance Segmentation: It not only defines the class of an object, but also distinguishes between different instances of the same class. For example, each car in the image will be highlighted as a separate object, which is especially important for tasks related to counting and identifying objects.
Panoptic segmentation: Panoptic Segmentation method combines semantic and Instance segmentation, providing complete understanding of the image. Panoptic segmentation recognizes both individual instances of objects and non-countable areas such as the sky, ground, or ocean. This allows you to analyze scenes more accurately and comprehensively.
Why do you need high-quality markup for semantic segmentation?
Properly labeled images allow models to effectively distinguish between objects and backgrounds, as well as correctly determine the boundaries and classes of objects in the image. As a result, semantic segmentation tasks improve the ability of computer vision systems to correctly perceive visual information. This is especially important in autopilot systems or medical diagnostics, where the slightest mistake can lead to tragic consequences.
Markup for semantic segmentation helps not only classify objects, but also determine exactly where each object is located, its boundaries and shape. This allows the machine to understand where each element in the scene begins and ends, which is important for more complex image analysis tasks.
How is semantic segmentation used in different industries?
Semantic segmentation is already actively used in various industries:
Autonomous cars: Autopilot technologies use semantic segmentation to recognize roads, pedestrians, and vehicles. This helps the car “see” the road situation and make the right decisions in real time, minimizing the risk of accidents.
Medicine: Semantic segmentation is used to process medical images such as MRI or CT scans. It helps to divide the image into different segments corresponding to tissues or organs, which improves diagnosis and increases the accuracy of medical reports.
In the case of X-rays or other medical images, segmentation models help highlight areas of interest such as tumors. Medical image analysis is of great importance as it automates the work of clinicians.
For example, Kvasir-SEG is an open dataset with images of gastrointestinal polyps and corresponding segmentation masks. Models such as the FCB-SwinV2 Transformer for segmentation, developed by Kerr Fitzgerald and Bogdan Matuszewski, can facilitate early detection of polyps that have the potential to develop into cancer.
Geographic information systems: Analysis of satellite images using semantic segmentation makes it possible to identify areas of land, reservoirs, forests, and urban areas. This helps in creating maps and monitoring changes in the environment.
Agro-industrial complex: Camera drones use semantic segmentation to analyze agricultural fields. This helps the farmer monitor the condition of the crops, identify areas with diseases or pests, thereby increasing the efficiency of field management.
Search for images using the virtual database: As already mentioned, semantic segmentation is the task of dividing an image into segments that share common features. This understanding allows the development of search algorithms that find images similar to the original, and opens up the possibility of creating visual search systems that find images based on content, not just metadata or text descriptions.
For example, if you want to find images of buildings with certain architectural features, the segmentation algorithm extracts and identifies those features in other images.
Agriculture: In agriculture, semantic segmentation makes it possible to analyze the condition of crops based on images from drones or satellites. Segmentation helps distinguish healthy and diseased areas of plants, allowing farmers to quickly respond to threats, diseases or pests. And with the help of segmentation, you can estimate the area under crops and predict yields to improve productivity.
Datasets and semantic segmentation: how to use them correctly
Creating accurate models for image segmentation is impossible without high-quality datasets in which each pixel represents a specific object or class. However, such segmentation requires much more detailed and voluminous data sets compared to conventional machine learning tasks. Not only the correct marking is important here, but also the variety of data, especially when safety depends on the model, as is the case with autopilots. Let's look at popular open datasets used for these purposes and their importance for training accurate algorithms.
Numerous open datasets exist for such tasks, covering a variety of semantic classes and providing thousands of examples with detailed annotations. For example, in the task of training an autopilot to recognize pedestrians, bicycles and cars, it is critical that the system clearly distinguishes all objects, otherwise accidents or false alarms are possible. Accuracy and reliability are critical here.
Here are some popular open source datasets for image segmentation:
Pascal Visual Object Classes (Pascal VOC): Includes various feature classes, bounding boxes, and segmentation maps.
MS COCO: Contains about 330,000 annotated images for detection, segmentation and description tasks.
Cityscapes: Focused on urban environments with 5,000 images, 20,000 annotations and 30 classes.
Semantic segmentation models
For excellent results in semantic segmentation, model architecture plays a key role. Not every neural network is capable of performing this task efficiently. Models like Fully Convolutional Networks (FCN), U-Net and DeepLab were specifically designed to handle every detail of an image at the pixel level. Let's take a look at how these models work, what makes them different from each other, and why they have become key tools for computer vision.
Fully Convolutional Networks (FCNs)
FCN is a neural network designed for semantic segmentation that uses convolutional layers to extract information about each pixel. Unlike standard CNNs, which use flat layers to produce single labels, FCN replaces them with convolutional blocks, extracting more data about the image.
Zooming in and out of an image: As convolutional layers accumulate, the image size decreases, losing spatial and pixel information. At the end of the process, the image is “restored” to its original size through upsampling.
Max pooling: This method selects the largest features in the analyzed region, keeping the most important features to create a feature map.
U-Nets
The U-Net architecture proposed in 2015 improves segmentation results compared to FCN. It includes two components: an encoder, which downsamples the image, and a decoder, which restores it through deconvolution. U-Net is often used in medicine for tumor recognition.
Direct connections: This is an important innovation in U-Net, allowing the output of one layer to be connected to another, non-contiguous one. It reduces data loss when downscaling, increasing the accuracy of the final result.
DeepLab
The DeepLab model, developed by Google in 2015, uses atrium convolutions to improve segmentation accuracy. Instead of standard resolution reduction, DeepLab stores more data, improving results through the CRF algorithm, resulting in more accurate segmentation masks.
Pyramid Scene Parsing Network (PSPNet)
PSPNet, introduced in 2017, uses a pyramid parsing engine to collect contextual information with greater accuracy. The architecture combines an encoder-decoder approach with pyramidal pooling, allowing it to analyze a larger volume of data and improve results.
All markup rules for semantic segmentation
Deep learning models typically require a huge number of input images to train. The process of creating a dataset involves collecting and labeling these images. At the same time, the markup process for semantic segmentation usually includes several key stages:
Stages of work:
Collection of raw data (images)
The first step is to collect images that will be used to train the model. Sources and data may vary depending on the task: it could be photographs of city streets for self-driving cars, medical images or satellite photographs. The quality and diversity of the source images play a key role, since they form the basis for the training set.Data Preprocessing
Before you start marking, images often require pre-processing. This may include resizing, color normalization, noise removal, artifact removal, and format conversion. This helps improve data quality and make it suitable for labeling and machine learning.Image markup
The most important step is marking each pixel of the image. The performer manually selects areas that correspond to certain classes (for example, person, car, building). This is a painstaking process that requires attention to detail, since the quality of the future model depends on the accuracy of the annotations.
In some cases, semi-automated tools or algorithms are used to simplify markup and speed up the annotation process. It often makes sense to use an existing segmentation model to automatically label images, and then manually correct errors and fill in missing areas.
In addition to using model predictions, edge detection algorithms and other segmentation methods can be used to pre-label the image. Next, pixels with the correct classes are manually populated, which helps reduce work and improve annotation accuracy.
Tools and software used
To make image tagging easier, there are many specialized tools and software solutions that help annotate images with high accuracy.
Popular markup tools
LabelMe: This is a free and widely used open source image tagging tool. It makes it easy to manually annotate images by creating polygons, lines, and other shapes to highlight objects.
VGG Image Annotator (VIA): Another free software solution that supports pixel-level annotation. VIA is convenient for quickly marking up images and supports many formats.
Automation software solutions
Modern technologies make it possible to automate part of the markup process, which is especially useful when working with large volumes of data. AI-powered software solutions can pre-label images, suggesting annotations that are then manually reviewed and refined.
SuperAnnotate: This platform combines automatic marking capabilities with manual adjustments, which greatly speeds up the process.
CVAT (Computer Vision Annotation Tool): A popular video and image tagging tool that automates processes based on pre-trained models, reducing annotation time and increasing accuracy.
Problems of semantic segmentation
In the case of semantic segmentation, its potential directly depends on the quality of data markup. From annotating rapidly changing scenes to the need to account for the cultural and geographic diversity of data, tagging presents a real puzzle. Let's look at common problems and discuss how to work around them.
Annotating dynamic scenes
Image tagging, especially in the case of semantic segmentation, is not just about highlighting objects, but about creating detailed maps where every pixel matters. But what happens when the scene is dynamic and objects are constantly moving?
Annotating video streams with moving cars, pedestrians or changing lighting conditions becomes a real challenge and requires high concentration and accuracy, since the slightest error in the labeling can reduce the quality of the model. Additionally, multiple viewpoints and complex object interactions complicate the annotation process, making it time-consuming and prone to human error.
Data diversification
In the world of semantic segmentation, data is the foundation of everything, and the more variety, the better the models learn. However, collecting and annotating images from a wide variety of cultural and geographic contexts is no easy task.
For example, how to mark city landscapes that differ in architecture, road signs, or plants? The more diverse the data, the more difficult it is to ensure consistent markup, as each new set can present unique challenges, from lack of image clarity to varying degrees of object visibility. This requires not only experience, but also a deep understanding of cultural and ethnographic characteristics.
The future of semantic segmentation markup
Automation, artificial intelligence, and self-learning models are changing traditional approaches to data labeling, making it faster and more efficient. But how far have we come? And what does the future hold for us? Let's talk about how new technologies affect markup and how they can transform entire industries, from medicine to agriculture.
Automation
Image tagging has always been a labor-intensive task, but as technology advances, AI-trained models are coming to the rescue. In the future, algorithms will be able to independently label images with minimal human adjustments.
But there is no need to be afraid, this does not exclude a person from the process completely. The role of specialists is changing – they become more of curators and validators, ready to intervene if the algorithms make a mistake. This will save time and resources, and the marking process will become even more efficient.
From manual work to intelligent automation
The future of semantic segmentation is full automation, where humans will play a significant role only in the final stages of verification. New approaches, such as the use of self-learning systems, will be able to recognize and classify objects with minimal dependence on training data.
Artificial intelligence will not only speed up marking, but will also take the entire field of computer vision to the next level with smarter and more intuitive models that can adapt to even the most challenging conditions.
These technologies don't just help businesses adapt to the future, they make that future a reality. Do you want to know how the rules of the game are changing, read more about markup and its use for business? Please pay attention to our other articles!