Instance segmentation: when AI differentiates between objects in an image
In the field of computer vision, instance segmentation is often considered an important technique because, while not automating the Data Labeling process as some might think, it allows the creation of complex and comprehensive datasets, allowing artificial intelligence models to recognize and differentiate each individual object in an image.
Unlike traditional object detection methods, which only locate general categories, instance segmentation aims to identify each distinct occurrence of an object, defining its precise contours and visually separating it from the others.
In industry, this capability opens up critical perspectives for a wide range of applications, from autonomous driving to medical analysis to industrial surveillance. By dividing each object into autonomous units, instance segmentation pushes the precision and sophistication of AI models to a new level, making it easier to interpret images that are more nuanced and similar to human perception.
💡 In this article, we explain how it all works and how instance segmentation techniques can make a difference in preparing your datasets for Computer Vision models!
What is instance segmentation and why is it unique?
Instance segmentation is an advanced computer vision technique that allows an artificial intelligence model to detect and differentiate each individual object in an image, visually separating it from any other object, even if it belongs to the same category.
Contrary to semantic segmentation, which identifies and groups all pixels belonging to the same category (for example, all “dogs”) without distinguishing between each individual, instance segmentation goes further by isolating each distinct occurrence of an object. This means that in an image containing multiple dogs, each dog is uniquely identified, with distinct outlines and a unique ID.
This approach is particularly unique and powerful because it brings AI closer to human-like visual perception, where each object is not only identified by category but also recognized as a distinct entity.
How is instance segmentation different from object detection?
Instance segmentation and object detection (Object Detection) are key computer vision techniques, but they differ in accuracy and visual output. Object detection identifies the position of objects in an image by surrounding them with bounding boxes, thus making it possible to locate general categories such as “car” or “pedestrian”.
However, it does not take into account the exact contours of objects and is limited to approximate locations, which is sufficient for applications where the precise shape is not essential.
On the other hand, instance segmentation provides additional refinement by delineating each object at the pixel level. It not only identifies the category of each object, but also traces its exact contours, even if there is an overlap.
This distinction is essential for applications where objects need to be analyzed individually and accurately, such as autonomous driving, where every pedestrian or vehicle needs to be recognized in detail.
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What is the difference between instance segmentation and semantic segmentation?
Instance segmentation and semantic segmentation are two methods of computer vision, but they serve different needs. Semantic segmentation classifies each pixel in an image according to the object category (for example, “car” or “tree”), without distinguishing between individual instances.
This means that in an image containing several cars, all the pixels representing cars are grouped together under the same label, forming a homogeneous whole.
In contrast, instance segmentation identifies each object as a unique occurrence. In the same example, each car would be isolated with its own contours, allowing for visual separation of each car, even if they belong to the same category.
This distinction is important in contexts where objects need to be differentiated individually, such as in medicine, where each cell in an image must be analyzed separately.
How does data annotation affect the quality of instance segmentation?
Data annotation plays a critical role in the quality of instance segmentation because it provides the accurate information that the model needs to learn to identify and differentiate each object in an image.
When training an artificial intelligence model, each object must be carefully annotated at the exact contours, with pixel-by-pixel precision, so that the model can detect each instance individually. The more accurate and well-defined the annotations, the better the model will understand the shape, position, and boundaries of objects under various conditions.
High-quality annotation helps avoid misinterpretations, such as confusion between objects that overlap or have similar shapes, and helps the model generalize correctly to new images.
In contrast, incomplete or approximate annotations can lead to segmentation errors, where the model misses details or confuses objects. In critical applications, such as autonomous driving or medical imaging, high-quality annotation is therefore essential to ensure reliable and safe model performance.
How does instance segmentation contribute to AI that is closer to human perception?
Instance segmentation contributes to making AI closer to human perception by allowing fine and individualized identification of objects in an image, an essential ability of our own vision. When a human observes a scene, he distinguishes not only the general categories of objects (such as cars, people, or buildings) but also each unique instance of those objects, even when they are similar or overlap.
Instance segmentation recreates this precision by delineating each object with its exact contours, allowing the AI to “see” and understand individual items in context. With this technique, AI models can analyze visual scenes in detail, differentiating each object at the pixel level.
This allows them to make finer and more appropriate decisions, whether by avoiding a specific pedestrian in autonomous driving, or by isolating a cancer cell in a medical image.
By approaching this individual and detailed perception, instance segmentation gives AI systems a more nuanced and contextual understanding of visual environments, making their actions and interpretations more accurate and reliable.
What are the main use cases for instance segmentation in AI?
La instance segmentation in AI is used in various fields requiring precise identification and distinction between individual objects. Here are the main use cases:
Facial recognition and biometrics
For security and personalization applications, instance segmentation can isolate the distinctive facial features of each individual, allowing for more accurate recognition and detailed analysis of facial expressions and emotions.
Autonomous driving
In autonomous vehicles, instance segmentation makes it possible to distinguish each pedestrian, vehicle, and obstacle, even when they are close or partially superimposed. This precision is essential for decision-making in real time, contributing to better road safety.
Medical imaging
Instance segmentation helps to isolate cells, organs, medical instruments, or tumors in medical images, making it easier to diagnose and monitor treatments. It allows health professionals to analyze each structure separately, thus improving the accuracy of analyses and interventions.
Industrial surveillance
In complex environments, instance segmentation identifies and tracks each object, part or machine, making it possible to detect specific anomalies or defects, and to ensure real-time monitoring in production chains.
Precision farming
Instance segmentation makes it possible to identify each plant in crop images, detect weeds, monitor plant health, and optimize cultivation practices by precisely targeting the needs of each agricultural area.
Satellite image analysis
Instance segmentation helps recognize and differentiate objects on the ground, such as buildings, vehicles, or crops. This is essential for resource management, environmental monitoring, and urban planning.
Augmented reality and video games
In interactive applications, instance segmentation makes it possible to integrate virtual objects precisely, recognizing each object in a scene to provide a more immersive and realistic experience.
Conclusion
Instance segmentation represents a major advance in the field of computer vision, as it offers artificial intelligence systems a finer and more individualized perception capacity.
By going beyond traditional detection and segmentation techniques, it makes it possible to analyze each object in an image as a unique entity, with precise contours and a clear distinction. This approach has applications in many sectors, from autonomous driving to medical imaging, where precision and object differentiation are essential.
With carefully annotated training data, instance segmentation allows AI models to approximate human perception, making their decisions more reliable and their understanding of visual scenes more detailed.
This ability of AI to isolate and analyze each object individually opens up promising perspectives for new applications, which require an intelligent and nuanced vision of the world around us.
