Table of Contents
- Semantic vs. Instance vs. Panoptic Segmentation: What Is the Difference?
- What Are the Main Applications of Semantic Segmentation?
- Semantic Segmentation with Deep Learning
- How to Annotate Data for Semantic Segmentation?
- Concluding Thoughts on Semantic Segmentation
We frequently discuss data annotation in our blog and how it fosters the development of today’s major industries. But what if we take an expert point of view and go into more detail about the strategies and tactics used to make machines even more intelligent with the help of labeled data?
You may read one of our articles where we covered all the key data annotation types. In this article, we’ll go into greater depth about one such data labeling method.
Semantic segmentation is one of the most challenging yet crucial data labeling tasks in machine learning, particularly in the computer vision domain. In essence, it’s the same old process of teaching machines to recognize different objects and scenes in semantic images or videos, which is a natural ability for humans.
Before we dig deeper into the topic, let’s define the issue first. Semantic segmentation is the process of assigning a class label to each pixel in an image (aka semantic classes). The labels may say things like “dog,” “vehicle,” “sky,” etc. The same-class pixels are then grouped together by the ML model. Semantic segmentation can be, thus, compared to pixel-level image categorization. As a result, each pixel of a picture is assigned a specific class label in semantic segmentation.
As the name implies, semantic segmentation means dividing an image into multiple segments. Sounds simple enough, right? However, to make the process run smoothly and let the machines learn as effectively as possible, semantic image segmentation is actually a multistep process that requires a variety of methods, models, and both ML and DL techniques. Every pixel in the picture is given an object type throughout this procedure. Image segmentation may be divided into two main categories: instance segmentation and semantic segmentation, which we’ll shortly discuss in the next section.
What advantages does semantic segmentation offer, then? In a nutshell, it builds a map containing clusters of different object classes that help the machine to better recognize the individual items in the image. So, it makes sense to learn more about semantic segmentation and master this method.
Our Label Your Data team is more than happy to help you with this and lay it all out!
Semantic vs. Instance vs. Panoptic Segmentation: What Is the Difference?
Image segmentation methods have been actively developed along with the rapid progress of AI over recent years. As a result, we now have three different approaches to image segmentation, each with its pros and cons.
In the past, image segmentation was inefficient on a big scale, but thanks to GPUs, cloud TPUs, and edge computing, its applications are now more accessible to the public. That said, let’s examine each of the three segmentation methods in more detail and see how they might be used both for AI research and in the real world.
Standard semantic segmentation, aka full pixel semantic segmentation, aims to assign a corresponding and unique class label to each pixel in an image, indicating what is being represented by that pixel. This task is also known as dense prediction, since we are predicting for each pixel in the image. The result of this method is essentially a high-quality image with each pixel assigned to a certain class, often the same size as the original image. So, it’s a pixel-level image classification.
Notably, semantic segmentation looks for the things that are uncountable in a picture. Each picture pixel is examined, and depending on the texture or category it represents, a distinct class label is given. Semantic segmentation methods that are frequently employed include SegNet, U-Net, DeconvNet, and FCNs.
The tasks involving countable items are typically the focus of instance segmentation. This method has the ability to identify every item or instance of a class visible in an image and gives it a distinct mask or bounding box with a special identification.
Let’s say you have a photograph of the road with vehicles and pedestrians on it. In semantic segmentation, all the pedestrians would have one texture (and given one class label) and all the vehicles would have another label (e.g., car). In instance segmentation, however, each vehicle would be labeled separately (e.g., car_1, car_2), as well as the pedestrians.
But these two methods have one thing in common, which is coherent scene processing.
There’s always a happy medium, even in image segmentation methods. In this case, we talk about panoptic segmentation. It offers a unified method in which each pixel in a scene is given a semantic label (semantic segmentation) and a special instance identification (instance segmentation).
Only one pair of semantic labels and an instance identification is given to each pixel in panoptic segmentation. Pixels may, however, overlap. To settle this conflict, the panoptic segmentation method favors the object instance.
To sum up, panoptic segmentation has gained increasing attention from researchers in an effort to develop computer vision. In contrast, due to the sophisticated nature of their techniques, both semantic and instance segmentation offer a wide range of practical applications.
What Are the Main Applications of Semantic Segmentation?
Image segmentation is a crucial process that brings hyper-automation across different sectors to reality. Semantic segmentation models need to operate quickly on mobile devices with low memory and processing capacity in order to be used in a variety of real-world applications. What are these sectors, and how is semantic segmentation applied across each?
The key applications for semantic segmentation include:
Semantic segmentation models provide highly useful scene understanding skills for various autonomous platforms (e.g., self-driving vehicles, drones, robots, etc.). For instance, semantic segmentation can identify lane lines and traffic signs, as well as offer information about open areas on the highways. The network must provide smooth segmentation since, in typical road scenes, the majority of the pixels are occupied by things like buildings or roadways. So there’s both academic and commercial interest in accurate segmentation models with little memory usage and quick inference.
To drastically cut down on the time needed to execute diagnostic tests, semantic segmentation has also made its way into the field of medical image diagnosis. Classifying abnormalities in CT scans is becoming a highly helpful tool for radiologists. The complexity of CT scans and the majority of medical images makes it challenging to spot irregularities. Image semantic segmentation can be used as a diagnostic tool to examine these images and help radiologists and doctors make life-critical decisions about the patient’s care.
Similar to the scene understanding process, aerial image processing also requires semantic segmentation of the terrain as seen from above. Drones (UAVs) may spread out to examine different locations in times of emergency, such as a flood or finding people and animals that need to be rescued. The use of aerial image processing in goods delivery is another viable example of semantic segmentation model application.
Semantic segmentation issues are classification issues. Therefore, there is a use case for mapping land usage in satellite imagery. Information on land cover is crucial for several purposes, including tracking urbanization and deforestation. Land cover classification can be thought of as a multi-class semantic segmentation task because it identifies the type of land cover (i.e., urban, agricultural, or water areas) for each pixel on a satellite image. In addition, for traffic management, city planning, and road monitoring, road and building detection is an essential study area.
With the help of semantic segmentation of crops and weeds, precision farming robots can minimize the number of herbicides that need to be sprayed out in the fields and help them immediately start weeding operations in real time. The agricultural sector may use these cutting-edge image vision tools to automate more tasks that previously required manual inspection.
Semantic Segmentation with Deep Learning
Simple colors and low-level texture information were the results of the early image segmentation algorithms. However, their performance was constrained by the accuracy of a segmentation approach as well as the constraints of the machine learning algorithm utilized (e.g., Support Vector Machine or Random Forest).
However, deep learning soon entered this game, bringing in parallel GPUs, large annotated datasets, and convolutional neural networks (CNNs). The latter was able to combine color, texture, and semantic data to provide results that were noticeably more accurate. As such, deep learning segmentation came into active use, with early approaches (vision models) focusing on CNN classification and producing a single semantic label for the image.
But the best method for segmenting images, with multiple objects classified and labeled, was about to be unveiled. This very need to find more precise and sophisticated ways to do this task led to the advent of semantic segmentation. Here, CNN image segmentation defines what pixel belongs to one of a predetermined number of semantic classes.
Multiple DL-based architectures have developed over time to enhance the semantic segmentation process. Let’s first look through some of the most frequently used techniques for reducing the latency and parameter count of semantic segmentation architectures, as well as improving their performance and semantic neural networks overall.
- Encoder-Decoder. A two-stage network that produces output by first decoding the properties of the input after they have been encoded.
- Multi-Branch. A semantic segmentation architecture, wherein the model handles inputs at two or more resolutions.
- Meta-Learning. Model architecture is set by a different learning model.
- Attention. An attention-based approach is used to aggregate the global context.
- Training Pipeline.By using better training/optimization methods, this architecture enhances existing designs.
- Downsampling and upsampling: One common method for creating image segmentation models is to use an encoder/decoder structure. It involves downsampling the spatial resolution of the input, creating lower-resolution feature mappings that are learned to be extremely effective at classifying objects, and then upsampling the feature representations to create a full-resolution segmentation map.
- Efficient CNNs: Fully convolutional networks are a diverse family of models that tackle numerous pixel-wise issues in image semantic segmentation. They increase accuracy by transferring classifier weights from pre-trained models, combining representations from many layers, and learning end-to-end on whole pictures. Transpose convolutions enable us to create a learned upsampling. Dilated convolutions offer an alternative method for preserving the entire spatial dimension while gaining a large field of vision.
- Residual connections: In addition to the long skip connections that already exist between the relevant feature maps of the encoder and decoder modules in the normal U-Net layout, the residual block also includes small skip connections (inside the block). With this technique, training may be completed more quickly and with deeper models due to the brief skip connections.
- Backbone architectures: As a feature extractor, many semantic segmentation models use one of many popular backbone networks. It’s frequently an architecture created for and occasionally pre-trained on a classification task and modified to generate features that may be scaled for segmentation.
How to Annotate Data for Semantic Segmentation?
Now a little of our Label Your Data team’s experience and a short semantic segmentation tutorial for you. To annotate data for semantic segmentation, you need to:
- Draw semantic segmentation on the image;
- Choose the “mask-to-polygon” option from the menu of your annotation tool;
- Turn segmentation into a polygon;
- Edit the polygon as usual, and use the “polygon-to-mask” tool to go back to segmentation mode.
*Keep in mind that this process varies, depending on the annotation tool you choose. In CVAT, for instance, you just create the polygon and select the mask as the output option. Other tools might require that in order to retrieve the segmentation mask, the frame should be annotated with the mask tool as opposed to a simple polygon.
However, manual semantic segmentation is a time-consuming and laborious task. Using the specialized tooling, our annotators closely follow each object’s outlines, which is quite challenging because irregular forms or areas where the edges of several items are difficult to tell apart create significant obstacles. Also, you can’t go fully automated with semantic segmentation tasks, otherwise, you risk losing the quality and accuracy of your annotations.
Therefore, creating an image segmentation dataset requires first labeling the data while taking into account each individual pixel, then sketching the exact form of the item, and labeling it as one would do in object detection. With that said, for semantic segmentation to be accurate and of the highest quality, you cannot go without expert annotators, specialized equipment, and a pinch of automation.
Concluding Thoughts on Semantic Segmentation
Semantic segmentation is a fundamental step in teaching machines to comprehend and analyze visual data in the same way as humans do. For the input image, it attempts to anticipate a dense labeling map that gives each pixel a distinct category label. As you can see, this skill that machines can develop using properly annotated data has a positive impact on many industries today.
Deep learning techniques have recently produced outstanding results in image semantic segmentation. We’ve examined some of the most common yet complex models and architectures that aim to enhance the performance and results of this intricate process.
However, merging global context data with fine-grained spatial detail is a persistent issue in semantic segmentation, particularly in the quest for effective real-time models when low-resolution representations are preferred. This continues to be a major pain point in the area, so we believe there’s a lot more that future research holds for us in the pursuit of advanced real-time semantic segmentation.
If you need a hand with semantic segmentation in your AI project, send your data to us. We offer secure and high-quality data labeling that is specifically tailored to your project’s needs and goals.
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