Polygonal annotation: defining the contours of advanced data annotation
Drawing polygons takes more time than using simple shapes like Bounding Box. That's probably what the majority of people think — especially DataPrepOps specialists or data scientists, of course! However, you'd be surprised to find out how easy creating a dataset for AI can be with polygonal annotation, and how the extra time invested in the annotation process can actually optimize the time needed to train models.
Annotation with polygons is an essential technique used for labeling, especially when preparing data for computer vision models. Whatever model you're looking to develop, it needs quality data to learn, and this polygonal annotation technique allows it to better understand and classify different objects when confronted with raw, unpublished, or new data. But that is not all. This guide will explain everything you need to know about polygonal annotation and help you understand how it contributes to the creation of quality datasets to train supervised models. Do you want to know more? Read on!
What is polygonal annotation and why is it important in data labeling?
Polygonal annotation is a technique used in data annotation, where annotators draw multi-sided shapes around objects of interest, in images or video footage, using a polygonal tool. This image segmentation process allows for better definition of objects.
This method is essential for training machine learning models to accurately recognize and understand various shapes and objects in datasets. By placing points precisely around the contours of an object, polygonal annotation makes it possible to deal with irregular shapes more effectively than a simple enclosing box.
This technique is widely used in fields such as autonomous vehicle technology, the analysis of satellite images, or even the analysis and diagnosis of medical imagery, where extreme precision is essential.
As machines become more and more important in our daily lives, polygonal annotation is becoming crucial for improving the learning of computer vision models and ensuring the reliability of artificial intelligence systems.
Why use polygonal annotation tools and techniques in your annotation processes?
Polygonal annotation should be preferred for its superior precision in identifying complex shapes within a data set. Unlike simple bounding boxes, which may include irrelevant background elements, polygonal annotation precisely follows the contours of the targeted object, reducing noise and improving the specificity of the data used to train machine learning models.
This precision is particularly important in fields such as agriculture, where drones analyze the health status of crops, or in the medical sector, where the precise identification of tumor contours can have a significant impact on diagnoses and treatment proposals.
In addition, the use of polygonal annotation makes it possible to optimize object detection tasks in dense urban environments, for autonomous vehicles, thus contributing to safer navigation by focusing only on the necessary data points and reducing the computational load.
With the constant increase in the volume of visual data (images and videos) that AI systems must process, polygonal annotation is an indispensable tool for improving the accuracy of models and, by extension, the performance and security of artificial intelligence applications.
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Key benefits of polygonal annotation
Polygonal annotation, or data labeling technique, offers numerous advantages for the development of AI-based products. From optimizing urban spaces to autonomous vehicles, these benefits are essential at all stages of building data sets and training AI models.
Here are the main benefits of polygonal annotation tools:
🎯 Better precision in object recognition
Polygonal annotation allows precise delineation of the real shape of objects, reducing background noise and focusing the training of the model on the target object. The result: better data quality and more reliable recognition in the real world.
🌀 Optimized for complex shapes
Unlike traditional enclosing boxes, polygons make it possible to capture the irregular contours of objects, which is crucial in areas such as health or fashion, where precision is essential.
🚀 Compatible with advanced AI applications
The more sophisticated AI becomes, the more accurately labelled data it needs. Polygonal annotation opens the way to complex use cases: autonomous driving, precision agriculture, medical diagnostics, etc.
⚙️ Faster and targeted treatment
Annotating only relevant areas reduces the amount of computing power needed, which is essential for real-time processing in complex environments, such as autonomous vehicle navigation.
🔒 Increased AI security and reliability
By providing greater detail and precision, this method improves the reliability of AI systems. This is particularly important in critical areas such as medical imaging or transport.
🔇 Reducing noise in datasets
Polygonal annotation limits unnecessary data by precisely isolating the object of interest. This makes it possible to provide targeted and better data for model training.
🤖 Automation potential
Thanks to advances in Computer Vision, tools can pre-annotate automatically complex contours. Polygonal annotation thus becomes faster and more profitable, while remaining supervised by humans.
How to use polygonal annotation for advanced labelling? Step by step
Polygonal annotation is an important process for refining the accuracy of AI models, especially when it comes to complex images. The following step-by-step guide looks at using polygonal annotation for advanced data labeling:
Step 1: Choosing the right images
Select clear images that clearly represent the scenarios in which the trained AI model will be applied. Make sure that these images contain the objects of interest and are varied enough to cover different cases that the AI might encounter.
Step 2: Define annotation guidelines
Develop comprehensive guidelines for annotators to follow to ensure consistency in how objects are marked. These guidelines should include how to deal with blackouts, reflections, and variations in lighting conditions that may affect the perception of the object's shape.
Step 3: Identify items of interest
Review the selected images and identify objects that require annotation. Appropriate identification at this stage is critical for the accuracy of data labeling and the image classification process.
Step 4: Annotate accurately
Using annotation tools, draw polygons around identified objects. Each point in the polygon must precisely follow the contours of the object, ensuring the creation of an adjusted and tightened polygon that excludes any background noise not related to the object.
Step 5: Revise and refine
Once the annotation is complete, perform a thorough review to check for errors or inconsistencies. Adjust polygons as needed to improve accuracy, ensuring that they accurately represent the shape of the object.
Step 6: Validate annotations
Have annotated images validated by a separate team or an automated system. This step is important to maintain consistent quality control of the data set and to confirm that the annotations respect the defined guidelines.
Step 7: Train the machine learning model
Feed the machine learning algorithm with annotated images. The precision of polygonal annotations contributes to increased model accuracy in recognizing and understanding diverse shapes and objects under varied conditions.
Step 8: Test and Iterate
Once the model is trained, test it with new images and assess its performance. Repeat this process, refining the polygonal annotations and continuing to train the model to continuously improve the accuracy of the AI system.
Thanks to the rigorous implementation of these steps, polygonal annotation can significantly improve the performance and reliability of AI models, especially in areas where precision is essential.
Real applications of polygonal annotation
In Data Labeling, the use of the polygonal tool is considerable in a wide range of concrete applications. It makes it possible to build better machine learning models and improve their ability to accurately represent the elements for which they are designed. From object detection to the prediction of complex shapes, polygonal annotation is useful in many real use cases, as shown below:
Medical image analysis
Polygonal annotation allows for detailed tagging of medical images, such as X-rays and MRIs. Doctors use it to identify pathologies with high accuracy. This can help save lives by detecting health problems more quickly and clearly.
Autonomous vehicles
Autonomous cars need to perceive the road like a human and make optimized decisions. Polygonal annotation helps by making traffic signs, lanes, and other vehicles visible to the on-board computer so that it can make safe driving decisions.
Agricultural monitoring with the polygonal tool
Farmers use drones to take photos of their crops. Polygonal annotation is used to indicate which areas of a field are healthy or diseased, making irrigation or treatment decisions easier.
In-store inventory management
In retail stores, polygonal annotation helps to track items on the shelves. The cameras can thus detect which products are present and which need to be restocked, helping to keep shelves full for customers.
Face recognition
Polygonal annotation is used to train computers to recognize faces, which can help find missing persons or unlock a phone with a simple look. It is essential that this be done carefully to protect the privacy of individuals.
💡 These examples show how polygonal annotation is critical in making AI smart and useful in daily life. It allows computers to better see and understand the world, which can make many tasks easier and safer for people.
A last word
Overall, polygonal annotation tools and techniques are essential for building the most advanced data models, which are not limited to image classification and object detection, but go well beyond that. This data labeling technique is used in many concrete areas of real life. We hope this article has helped you better understand polygonal annotation. If you have questions about your data annotation process, do not hesitate to contact us.
