Polygon, Mask, or Keypoint for Image Segmentation

In computer vision, there is no universal way to isolate objects, as each task dictates its own requirements for accuracy and resources. While a simple bounding box is sufficient for counting cars in a parking lot, autonomous driving or medical diagnostics require knowing the exact boundary of an object down to every pixel. This is where the need for different image segmentation methods arises.

Each approach is a compromise between three factors: data preparation speed, annotation cost, and model training complexity. Understanding the differences between them allows developers to choose the most effective tool for a specific scenario, where sometimes the general shape of an object is important, and other times, its internal structure or perfect edge precision.

It is also important to understand the level of detail: while semantic segmentation combines all objects of the same type into one group, instance segmentation isolates each unit separately. Choosing the right method allows for project budget optimization without losing the accuracy vital for the neural network.

Quick Take

• Unlike rectangles, segmentation provides an understanding of shape, which is critical for medicine and autonomous vehicles.
• Polygon is the best choice for distinct objects, balancing price and quality.
• A mask is needed for complex, amorphous shapes, but it significantly increases project costs.
• Keypoints are indispensable for movement and pose analysis, focusing on nodal points.
• Unnecessary detailing burns the budget, while excessive simplification leads to AI errors in real-world conditions.
• Combining methods is the smartest business strategy. For example, polygons for the background and masks for objects.

Visual Labeling Tools

To teach artificial intelligence to distinguish objects, annotators use various geometric tools. The choice of a specific method depends on how detailed the computer must identify the edges or structure of the item.

The Essence of the Presented Annotation Methods

Each approach can be compared to different ways of drawing, where each tool has its own purpose and level of complexity.

• Polygon involves placing points along the contour of an object, which are connected by straight lines. This method allows for the creation of a flexible boundary around the item, following its shape. It resembles tracing a drawing with a pencil along the edges. Such boundary annotation is ideal for objects with clear contours.
• Mask is the pixel-by-pixel coloring of an object. Unlike a polygon, a mask does not just trace a boundary but fills the entire area of the item with color. This is the most data-intensive method, allowing every millimeter of the shape to be conveyed to the model, which is especially important, for instance segmentation, where it is necessary to distinguish similar objects that overlap each other.
• Keypoint involves marking only the critically important nodes of an object instead of its boundaries. For example, we place points on a person's elbows, knees, and shoulders. This does not describe the body shape but provides an understanding of its pose and position in space.

Choosing a Method Depending on the Task

Different types of annotations are used in projects depending on which information is a priority: edge precision, general class description, or internal structure.

For tasks where maximum detail of every pixel is vital, mask R-CNN training is often used, requiring meticulous coloring. Meanwhile, polygons remain the gold standard for most commercial projects because they are easier to process and faster to execute.

Balancing Accuracy and Efficiency in Development

Every segmentation method is a compromise. Choosing the wrong format can lead to either insufficient model accuracy or overspending the budget on data labeling that the artificial intelligence cannot effectively use.

Accuracy vs Labeling Complexity

The level of detail directly affects the time an annotator spends on a single frame. Keypoints are placed instantly, while a high-quality mask may require tens of minutes of painstaking work on a single object.

• Key points. Provide the lowest edge detail but the highest stability in conveying structure. This is a stable method where annotator error is minimal.
• Polygons. Offer high contour detail. Stability depends on the "density" of points: if one labeler places 10 points on a wheel and another places 50, the model receives inconsistent data.
• Masks. Provide maximum pixel accuracy. This is the most difficult method, where the annotator must literally "feel" the object's boundary, often leading to fatigue and minor errors at the edges.

Impact of Method Choice on Model Quality

The type of segmentation determines how well the neural network can generalize knowledge. For example, mask R-CNN training based on pixel masks allows the model to better understand complex object overlaps, such as when one car partially obscures another.

If overly coarse polygons are used for small objects, the model will learn to capture bits of the background, leading to recognition errors in real-world conditions. On the other hand, excessive mask detailing for simple objects can overload the algorithm with unnecessary computations without adding real value to forecast accuracy. A correctly chosen method helps the model focus on the main thing without being distracted by visual noise.

Cost and Scaling of Annotation

When planning large projects, it is important to consider how the price per frame increases depending on the chosen tool. Scaling from a thousand to a million images requires a clear understanding of the budget.

Moving from polygons to masks usually doubles or triples project costs. Therefore, mixed approaches are often used for savings: the bulk of the data is marked with polygons, while the most critical or complex cases are marked with precise masks. This allows for a high-quality model without astronomical data preparation costs.

Selection Strategy and Workflow Optimization

A correctly selected labeling method is the foundation upon which the entire model architecture is built. A mistake at this stage can cost months of extra work or result in a model that cannot handle real-world tasks.

When to Combine Methods

In complex projects, it is rare to limit oneself to just one type of annotation. Often, a combination of approaches gives the system much more context for understanding the scene.

• Polygons + Keypoints. Frequently used in retail or medicine. A polygon describes the general shape, while points fix the positions of joints. This helps the model understand not just "where the object is," but also "what it is doing."
• Masks + Polygons. For autonomous driving, critical objects may be labeled with masks for perfect precision, while background buildings or trees are marked with simplified polygons to save budget.
• Bounding Boxes + Segmentation. First, the model finds the object within a box, and then another model refines its boundaries inside that box using a mask.

Typical Mistakes in Method Selection

Most mistakes in selecting an annotation format are related to an incorrect assessment of project needs at the very start. The main problem is a lack of balance between the necessary detailing and the real capabilities of the model. Often, developers strive for maximum accuracy where it does not affect the end result, leading to inefficient resource use and delayed development timelines.

One of the most common mistakes is choosing overly complex labeling. For example, using pixel masks for objects with simple geometric shapes where regular polygons would have been entirely sufficient. This not only significantly inflates the data preparation budget but also creates an additional load on computing power. The model has to process massive amounts of redundant pixel data that carry no new useful information for object recognition.

The other extreme is the excessive simplification of annotation for complex scenarios. Attempting to label flexible, thin, or translucent objects, such as electrical wires or tree branches, with coarse polygons with few points leads to serious training defects. In this case, the model begins to perceive the void between points as part of the object itself. This becomes a vulnerability for safety systems or autonomous driving, where inaccurate determination of an obstacle's boundary can lead to a collision.

How to Choose a Method for Your Project

To make the right decision, you should analyze three key factors of your task:

1. Object Geometry. If edges are clear and straight (buildings, signs), choose a polygon. If shapes are amorphous and complex (smoke, liquids, vegetation), a mask is necessary.
2. Training Goal. If you need to understand movement and pose, your choice is keypoint. If it is important to know the exact area and every indentation, use a mask.
3. Budget and Timelines. Always start with the simplest method that meets the minimum requirements. It is better to have 100,000 high-quality polygons than 5,000 perfect masks on which the model simply won't have time to learn general rules.

FAQ

How does lighting quality affect the choice between polygon and mask?

In poor lighting or when shadows are present, object boundaries become blurred. In such cases, polygons can be "safer" because the annotator can logically complete a straight line. Masks, however, require a clear vision of every pixel, so on dark frames, they often lead to noisy and inaccurate labeling that confuses the model.

What is "OCD" in annotation, and how does it hurt a project?

This is the tendency of annotators toward excessive detailing. If your model does not require such precision, this perfectionism becomes an enemy: it slows down labeling and creates oversaturated data that the model will filter out as noise anyway.

What to do with objects that overlap each other?

Instance segmentation using masks is ideal for such cases. Polygons are difficult to draw when one object cuts another into two visible parts. Masks allow for marking two separate fragments as the same instance, helping the model understand that the object is a single whole, just obscured by an obstacle.

Are there file formats that support all these types simultaneously?

Yes, the most popular format is COCO. It is a JSON file that can simultaneously store polygon coordinates, keypoints, and references to masks. This makes it a universal standard for most modern architectures, such as Mask R-CNN.

Are there ways to automatically convert polygons to masks and vice versa?

The process of converting a polygon to a mask is quite simple and comes down to a simple outline filling. However, the reverse operation – transforming a mask to a polygon – is much more complicated. It requires the use of line simplification algorithms to avoid creating a polygon with thousands of unnecessary points that would overload the model training system.