Semantic Segmentation vs. Instance Segmentation: What’s the Difference?

Image segmentation techniques are an important component of computer vision algorithms, which help machines interpret visual information by dividing it into meaningful sections and classifying it. While semantic and instance segmentation approaches identify and label objects in images, they each do so in unique ways. When you have a deeper understanding of the differences between these algorithms, you can select the proper technique based on the needs of your specific computer vision application.

What is semantic segmentation?

Semantic segmentation is a computer vision algorithm that labels every pixel in an image by its semantic class. This means that instead of identifying individual objects, semantic segmentation focuses on labeling object classes across the entire image and grouping similar pixels together. This is particularly useful in landscape scenes, where the algorithm can assign labels such as “mountain,” “river,” and “vehicle” to help contextualize the image as a whole.

What is semantic segmentation used for?

Because semantic segmentation provides more of a holistic view of object classes rather than focusing on specific individual detection, it’s widely used for applications that benefit from broad classification of object types. For example, common uses include:

Autonomous vehicles

Semantic segmentation helps self-driving cars understand their surroundings by identifying classes such as “lane,” “pedestrian,” or “vehicle.” When the algorithm doesn’t need to differentiate between individual pedestrians or vehicles, semantic segmentation is the most common choice to help cars navigate the roads safely, recognize obstacles, and be aware of their environment. 

Robot vision

Similar to self-driving cars, robots equipped with vision systems use semantic segmentation to recognize and interact with their environment. For instance, a robot vacuum might categorize objects into classes such as “furniture” or “wall” to navigate efficiently and avoid obstacles. In a medical setting, particularly in robotic surgery, the robot relies on semantic segmentation to identify and distinguish between categories like “surgical instruments” and various anatomical locations to ensure safety and precision during procedures. 

Environmental mapping

Semantic segmentation is also helpful for large-scale analyses, such as analyzing satellite imagery. These data sets are too large to label individual instances, and the ability to quickly segment images into broader categories such as “sparse forest,” “dense forest,” “building,” “road,” and so on can help professionals interpret trends in big data sets more effectively.

Advantages of semantic segmentation

Semantic segmentation has several benefits, particularly for tasks that require a high-level interpretation of visual data. By focusing on object classes instead of individual instances, semantic segmentation is computationally efficient, meaning it can process visual imagery quickly. It’s vital for applications requiring real-time decision-making, such as robotic surgeons or self-driving cars. 

Disadvantages of semantic segmentation

Semantic segmentation algorithms cannot distinguish different instances of the same object. For example, if you analyzed an image of traffic, the algorithm would label each vehicle as “vehicle” but could not discern between individual objects or know the exact location of each vehicle. 

When it comes to overlapping objects, semantic segmentation algorithms also struggle to accurately label or count the items present. In addition, semantic segmentation often relies on labeled training data, so the algorithm learns how to detect predefined classes accurately. This can limit the classes that the algorithm can recognize, which can introduce challenges in real-world scenarios. For example, an autonomous vehicle driving on an open road may encounter unforeseen obstacles or environments.

What is instance segmentation?

Instance segmentation is another computer vision algorithm, but unlike semantic segmentation, it goes beyond object classes to distinguish between individual instances of each object type in the image. Each instance of an object in a category gets a unique label, allowing the algorithm to recognize multiple distinct objects of the same kind. For example, if your algorithm assesses a picture of traffic, it would give each car its own label to differentiate them as separate entities. 

The ability to distinguish between objects is useful for applications that require detailed information about objects in a scene. Whereas semantic segmentation gives a general understanding of the environment, instance segmentation provides a more detailed view.

What is instance segmentation used for?

You can use instance segmentation for many of the same applications as semantic segmentation. Still, individual object detection allows it to interact with multi-object scenes more specifically. Ways you might see instance segmentation used include:

Autonomous vehicles

Many autonomous vehicle algorithms use instance segmentation in addition to semantic segmentation to perform object detection and segmentation concurrently. After semantic segmentation “sets the scene” by labeling broad categories, instance labeling takes this a step further by labeling distinct objects within each category, such as differentiating between multiple cars or pedestrians at the scene. This combination is essential to accurately interpret the visual information, allowing computer vision algorithms to understand the depth and location of objects around them. 

Medical imagery

When it comes to medical imagery, the challenge of crowded or overlapping cells, tissues, and other anatomical structures makes it difficult for semantic segmentation algorithms to differentiate between the classes accurately. Instance segmentation algorithms excel in these scenarios by identifying and separating unique instances within a class, creating a more detailed representation of the medical image. An instance segmentation algorithm can detect and count individual cells, even if they cluster or overlap. 

Robot vision

With instance segmentation, robots can detect and differentiate individual instances of objects with high precision, allowing them to perform more tailored actions. This capability is instrumental in agriculture, where robots equipped with instance segmentation can identify specific plants and discern between crops and weeds. By isolating each plant, these robots can selectively target and remove weeds, apply pesticides, and create detailed maps of crop types.

Advantages of instance segmentation

Instance segmentation offers several advantages, especially in applications relying on individual object identification and precision. One of the primary benefits is the ability of this algorithm to recognize each instance of an object, enabling tasks like counting, tracking, and detailed analysis of multiple objects within the same class. This is especially beneficial for medical imaging and object detection. 

Disadvantages of instance segmentation

Despite its benefits, instance segmentation has several limitations, primarily due to its computational demands and data requirements. Processing individual instances of objects requires significant computational power and memory, often relying on high-quality training data, with each object clearly labeled and defined with exact boundaries. These training data sets can be time-consuming and resource-intensive to create and sometimes require specialized annotation software.

How to decide the right segmentation technique for you

When deciding between semantic and instance segmentation, it’s crucial to consider your application's specific requirements and the level of detail required. For applications that need a general classification of object types, you might opt for semantic segmentation. This approach is often faster and less computationally intensive, making it suitable for things like navigation and high-level environmental mapping.

If you want more detailed insight into individual objects, you might choose instance segmentation for its ability to perform object-level tracking, counting, and targeted interactions. However, this approach requires higher-quality training data and more computational power, which can raise costs and processing times. 

Start learning computer vision on Coursera.

Semantic and instance segmentation are two powerful computer vision algorithms. Semantic segmentation identifies broad object classes, while instance segmentation allows computers to identify individual objects within a class. While this is a key topic in computer vision, it’s one of many foundational concepts you can explore with exciting computer vision courses on Coursera. As a beginner, consider completing the five-course First Principles of Computer Vision Specialization offered by Columbia University on Coursera.