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Knowledge

Semantic segmentation in AI, principle and applications

Written by
Daniella
Published on
2024-06-06
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0
min

Introduction to Computer Vision

Computer vision is a dynamic field of artificial intelligence focused on enabling computers to interpret and understand visual information from the world around us. By leveraging advanced algorithms and statistical models, computer vision systems can process, analyze, and extract meaningful insights from digital images and videos. This technology forms the backbone of many modern applications, from image segmentation and object detection to image classification and medical image analysis.

In practical terms, computer vision allows machines to perform tasks that require visual understanding, such as recognizing objects in a photograph, analyzing medical images for signs of disease, or interpreting scenes for autonomous vehicles. The ability to analyze and understand visual data not only enhances the capabilities of automated systems but also opens up new possibilities in fields like healthcare, security, and entertainment. As a result, computer vision has become an essential component in the development of intelligent systems that interact with the visual world.

Semantic segmentation: how does it transform our vision of the world?

Semantic segmentation is at the heart of advances in computer vision and artificial intelligence. As a core task in the broader field of semantic image segmentation, it represents one of the most relevant image processing methods for understanding and interpreting visual scenes.

By segmenting an image into different regions and by assigning to each pixel a label corresponding to its semantic class, this technique allows fine and accurate analysis of visual content. Semantic segmentation also identifies different parts of the image, including the background, which improves the accuracy of the analysis.

Since its first uses in the 2000s, semantic segmentation has undergone significant development, driven by advances in machine learning algorithms and neural network architectures and deep neural networks. Advances in machine learning techniques, such as fully convolutional networks and encoder-decoder architectures, have driven progress in the field. Neural networks, especially architectures CNN, FCN, U-Net, DeepLab and PSPnet, play an essential role in training and structuring semantic segmentation models. FCN stands for fully convolutional networks, which are a type of convolutional network designed specifically for semantic segmentation by enabling pixel-wise predictions through up-sampling and down-sampling processes. These are examples of deep learning models and convolutional networks that have revolutionized semantic segmentation. DeepLab, in particular, utilizes the feature space generated by atrous convolution to capture fine details for accurate segmentation.

This evolution has opened up new perspectives in fields as varied as autonomous driving, medicine, cartography or even augmented reality. More details in this article!

What is semantic segmentation and how does it work?

Semantic segmentation is an image processing technique which consists in dividing an image into different regions and in assigning to each pixel a label corresponding to its semantic class. This technique makes it possible to classify pixels into different classes, thus facilitating the understanding of the image. Semantic segmentation is closely related to classification tasks, but operates at the pixel level, where the classification task involves assigning a class to each pixel in an image. The process of labeling each pixel in an image is based on its characteristics such as color, intensity, or texture, enabling accurate partitioning of the image. To improve the accuracy of segmentation, it is often useful to use a set of predefined classes or a set of specific data.

In other words, it makes it possible to understand what each part of the image represents. To do this, semantic segmentation uses machine learning algorithms, in particular deep neural networks.

They are trained on large amounts of data to recognize and classify the various visual elements. Organized and accurate input data is crucial for helping models better understand and classify image segments, ultimately improving prediction performance. The training data often consists of labeled pixels, which provide essential domain knowledge for the model to learn specific features and improve segmentation accuracy. They are able to learn to identify specific features in an image, such as contours, textures, and colors. The features extracted during training play a significant role in determining the effectiveness and accuracy of the segmentation. Additionally, neural networks identify different parts of an image, including the background, by analyzing spatial and contextual relationships between pixels. Preserving spatial information is important for accurate segmentation, as increasing network depth can lead to a loss of pixel-level spatial information, which is often addressed by using deconvolution layers for upsampling. This is what allows them to segment the image according to its semantic content. To achieve detailed, pixel-level precision, a pixel wise loss function is often used during training, ensuring that the segmentation output closely matches the ground truth at the pixel level.

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Types of Image Segmentation

Image segmentation is a foundational task in computer vision that involves dividing an input image into multiple segments or regions, each representing a meaningful part of the scene. By breaking down an image into its constituent parts, image segmentation enables more detailed analysis and understanding of visual data, which is crucial for a wide range of computer vision applications.

There are several types of image segmentation, each designed to address specific challenges and use cases. The main approaches include semantic segmentation and instance segmentation, both of which play a vital role in extracting valuable information from input images.

Semantic Segmentation

Semantic segmentation is a powerful image segmentation technique that assigns a class label to every pixel in an input image. This process, often referred to as dense prediction, ensures that each pixel is categorized according to a predefined set of object classes, such as road, building, or person. Semantic segmentation models are widely used in applications where understanding the precise layout and composition of a scene is essential.

In the field of medical image segmentation, for example, semantic segmentation models help identify and delineate structures like organs, tumors, or lesions within medical images, supporting accurate diagnosis and treatment planning. Beyond healthcare, semantic segmentation is also used in image analysis for autonomous vehicles, where it enables the system to distinguish between different elements of the road scene, and in image captioning, where understanding the context of each region enhances the generation of descriptive text. By providing pixel-level classification, semantic segmentation models deliver detailed segmentation maps that are crucial for a variety of image analysis tasks.

Instance Segmentation

Instance segmentation takes image segmentation a step further by not only classifying each pixel but also distinguishing between individual objects of the same class within an input image. This means that the segmentation model can identify and separate multiple instances of the same object category, such as several cars or people in a single scene.

This approach is particularly valuable in object detection and image analysis, where it is important to recognize and analyze each object separately. For example, in robotics and automated systems, instance segmentation enables precise manipulation and interaction with different objects, even when they belong to the same class. By generating segmentation masks for each detected instance, instance segmentation models provide a more granular understanding of complex scenes, supporting advanced computer vision applications that require detailed object-level analysis.

What are the main areas of application of semantic segmentation?

Semantic segmentation has varied applications in a number of different areas, including:

  • It is commonly used in computer vision for object recognition and image classification. For example, in the medical field, it makes it possible to segment radiological images to identify anomalies. In these domains, model performance is critical, as high-quality image segmentation models are required to ensure precise and reliable results. In the automotive industry, it is essential for the development of AI used by autonomous vehicles, helping to detect and classify objects on the road. Here, advanced image segmentation models and robust segmentation algorithms play a key role in achieving accurate segmentation results and enhancing safety.
  • In addition, semantic segmentation often uses predefined data sets or sets of classes to improve the accuracy and efficiency of algorithms. The choice of segmentation algorithm and image segmentation model can significantly impact the efficiency and accuracy of the process, as different segmentation techniques and architectures are designed to optimize segmentation results for specific tasks.

Computer vision applications and recognition of objects in an image

Semantic segmentation plays an important role in computer vision by allowing the accurate detection and classification of objects in images. Object detection models often use bounding boxes to localize objects within images, providing approximate object locations, whereas semantic segmentation assigns a class label to each pixel for more precise delineation. By segmenting an image into semantically significant regions, this technique allows computer vision algorithms to understand the composition of the scene and identify each object present. Unlike object detection models that rely on bounding boxes for spatial localization, semantic segmentation provides detailed, pixel-level segmentation of objects.

It also distinguishes background objects by using segmentation masks to isolate regions such as the ground, sky, or other elements from the main object. Deep learning plays a key role in this process, allowing semantic segmentation models to effectively identify different parts of an image, including the background.

This is especially important for applications such as video surveillance. In this field, fast and accurate object detection can be critical for safety. The same is true for autonomous cars, where semantic segmentation is used to detect and identify pedestrians, vehicles, and obstacles on the road.

Mapping and navigation

In cartography, semantic segmentation is used to create accurate and detailed maps by automatically identifying the various elements of a scene, such as roads, buildings, trees, and pedestrian areas.

This precise segmentation is essential for creating digital maps used in GPS navigation, urban planning, and natural resource management.

In the field of navigation, semantic segmentation is also used to help robots and autonomous vehicles interpret their environment by identifying obstacles and planning safe trajectories. Integrating semantic segmentation with an automated system enables real-time interpretation and response to environmental changes, enhancing the adaptability and efficiency of navigation and robotics.

Medicine and medical imaging

In medical imaging, semantic segmentation is used to automatically segment and identify the various anatomical structures in which medical images appear, such as organs, tumors, or blood vessels.

Semantic segmentation models are also used to detect anomalies, such as tumors or abnormal tissue, in medical images like MRI and CT scans, supporting the identification of cancer cells and other abnormalities.

This precise segmentation is essential for the diagnosis of diseases, the planning of treatments and the monitoring of the evolution of pathologies, in the context of the development of medical AI.

For example, in magnetic resonance imaging (MRI) and AI models developed around this technology, semantic segmentation is used to identify and measure the shape and size of brain tumors, which helps doctors assess disease progression and plan treatments.

Satellite image analysis and terrain recognition

Semantic segmentation is widely used for the analysis of satellite images by automatically identifying different types of terrain, such as forests, waterways, urban areas, and agricultural land. Analyzing images at multiple scales or different scales improves the accuracy of terrain classification and change detection, as it enables the detection of features and objects of varying sizes and resolutions.

This precise segmentation is useful for environmental mapping, natural resource monitoring, land management, and urban planning. For example, in the field of environmental monitoring, semantic segmentation is used to detect changes in land cover.

It makes it possible to detect deforestation, urbanization and the erosion of materials and soils. This allows researchers (and sometimes policy makers) to effectively monitor and manage fragile ecosystems.

Virtual and augmented reality

In virtual and augmented reality, semantic segmentation is used to recognize and segment an object and surfaces in the real world. This allows augmented reality applications to incorporate virtual objects in a realistic manner into their environment. Complete scene segmentation further enhances these experiences by providing detailed labeling of all objects and surfaces, enabling more immersive and interactive environments.

For example, in augmented reality video games, semantic segmentation is used to detect flat surfaces, such as tables and floors. A virtual object can then be placed there in a realistic manner. This is the guarantee of an immersive experience for players.

Likewise, in virtual reality applications, semantic segmentation is used to detect obstacles and objects in the virtual environment, allowing users to interact realistically with their virtual environment.

Semantic segmentation: a bridge between human perception and artificial intelligence?

Semantic segmentation plays an essential role in bringing artificial intelligence closer to the understanding and interpretation of visual scenes. Semantic segmentation tasks are designed to provide detailed scene understanding, similar to human perception. This opens up new perspectives in areas such as computer vision, robotics, and augmented reality.

For comprehensive scene understanding, panoptic segmentation combines the strengths of semantic and instance segmentation to deliver a unified analysis of both discrete objects and background elements. Panoptic segmentation bridges the gap between semantic segmentation tasks and complete scene understanding by integrating both object-level and background information within a single framework.

Similar understanding of the environment

Semantic segmentation allows AI to understand visual scenes in ways similar to human perception. It can split an image into different regions and assign semantic meaning to each pixel by labeling them with specific semantic classes, such as cars, pedestrians, or trees. This enables detailed categorization of objects and elements in the scene, allowing algorithms to recognize and categorize them in the same way as a human being would.

Contextual interpretation

Since humans interpret a scene taking into account the context and relationships between the various elements, semantic segmentation also allows AI to analyze images in a contextual way. By identifying spatial and semantic relationships between objects, it allows algorithms to understand the overall meaning of the scene and to act accordingly. Semantic segmentation uses class labels to differentiate between objects and regions based on their contextual relationships, enabling more accurate interpretation of complex scenes.

More natural interaction

By understanding visual scenes in a manner similar to human perception, semantic segmentation makes the interaction between humans and machines more natural and intuitive.

For example, in augmented reality applications, segmentation at the semantic level allows algorithms to detect flat surfaces and obstacles. Semantic segmentation analyzes the entire image to ensure accurate placement and interaction of virtual objects within the scene. As previously mentioned, this allows them to place virtual objects more realistically, making the user experience more immersive and satisfying.

Semantic segmentation: what are the prospects for the future of technology?

Semantic segmentation has promising potential to shape the future of technology at multiple levels. State of the art semantic segmentation models continue to push the boundaries of what is possible in computer vision.

Future improvements will likely focus on more effective extraction and utilization of image features, enabling next-generation segmentation models to achieve even higher accuracy and robustness.

Additionally, the integration of semantic segmentation with other technologies opens the door for other methods, such as generative models or open-vocabulary approaches, to further enhance segmentation capabilities.

Improving the perception of machines

Semantic segmentation will continue to improve the ability of machines to perceive and understand their environment in ways similar to human perception. While earlier approaches like support vector machines were once widely used for image segmentation, they have largely been replaced by deep learning models due to their superior performance. This will pave the way for significant advances in areas such as robotics, autonomous driving, and augmented reality. This technique could allow machines to interact smarter and more intuitively with the world around them.

Development of new applications

Semantic segmentation will open the way to new dimensions and innovative applications in areas such as health, education, agriculture, urban planning and the environment. For example, it could be used to monitor the condition of agricultural crops, analyze medical images to diagnose diseases, or even to assess the impact of climate change on the environment.

In these fields, corresponding segmentation masks are used to delineate regions of interest, such as tumors in medical images or infected crops in agriculture, enabling automated and accurate analysis.

Integration with other emerging technologies

Semantic segmentation will increasingly be integrated with other emerging technologies such as the Internet of Things (IoT), virtual reality (VR), and blockchain. The encoder decoder architecture is a foundational component for integrating semantic segmentation with these technologies, enabling efficient feature extraction and precise output reconstruction. This technological convergence will open up new opportunities for innovation and value creation in areas such as logistics, security, entertainment and e-commerce.

What are the ethical implications of using semantic segmentation?

The use of semantic segmentation raises complex ethical questions that require careful thought and appropriate regulation to ensure its responsible and ethical use in society. Pixel value information, which is fundamental to image processing and segmentation techniques, can be sensitive and must be handled with care to protect privacy.

Protection of privacy and personal data

Semantic segmentation can be used to extract sensitive information from an image, such as facial recognition or mass surveillance. This raises concerns about privacy and the risk of intrusive surveillance.

It is essential to have strict policies and regulations in place to ensure that personal data is not misused.

Bias and discrimination

Like any machine learning algorithm, semantic segmentation models can be subject to bias, reflecting the biases in the training data.

This can lead to discriminatory or unfair results, by favoring certain groups or marginalizing others. It is key to implement techniques to mitigate bias and to ensure transparency and fairness in the design and use of these models.

Accountability and automated decision making

In some fields, such as autonomous driving or medicine, semantic segmentation is used to make critical decisions that can have a direct impact on people's lives.

This raises questions of liability in the event of a system error or failure. There is a need to clarify the legal and ethical responsibilities of developers, manufacturers, and users of these automated systems.

Impact on employment and professions

The increasing automation of tasks through technologies such as semantic segmentation can lead to economic and social disruptions, changing job requirements and replacing certain jobs.

It is important to put in place professional retraining and social protection policies to mitigate the negative effects on affected workers.

Conclusion

Semantic segmentation is essential in the field of computer vision and deep learning, offering significant advances in understanding and interpreting visual scenes. Its diverse applications, ranging from autonomous driving to medicine, open up new technological and societal perspectives.

However, while semantic segmentation offers many exciting perspectives, it also raises technical, ethical, and social challenges. It will be more important than ever to develop advanced techniques to overcome the current limitations of semantic segmentation, such as precise segmentation in low light conditions or in complex environments.

In addition, it will be critical to address ethical challenges related to privacy, transparency, and fairness in the use of this technology!