What Is Polygon Annotation and How Does it Work?

Polygon annotation is an essential labeling technique for supervised computer vision (CV). 

Objects are labeled with polygon annotations to create a dataset, which is fed into a supervised CV model. The model learns from the annotations, enabling it to predict and classify objects when exposed to new, unseen data.

The physical environment primarily consists of complex shapes with non-linear edges – polygon annotation is considerably more effective at labeling them when compared to bounding boxes, which include a lot of useless information.

Here’s all you need to know about polygon annotation.

Understanding Polygon Annotation

At its core, polygon annotation involves outlining objects in images using polygonal shapes, enabling algorithms to differentiate between objects and backgrounds.

Traditionally speaking, polygon annotations were used for image segmentation, which differs from object detection (image segmentation models seek to find the pixel boundaries between objects, whereas object detection aims to locate objects).

While it may appear that polygon annotation and forms of image segmentation, like semantic segmentation, are the same, polygon annotation still uses coordinates, whereas semantic segmentation labels at the pixel level.

The Benefits of Polygon Annotation

Polygon annotation is sensible for annotating complex and non-linear shapes and objects for image segmentation or object detection tasks.

Here are the key benefits of polygon annotation:

1: Precision and Accuracy

Polygon annotation enables the precise labeling of objects within images.

By carefully drawing polygons around objects, annotators can capture the true shape and boundaries of objects with a high degree of accuracy. This results in better-quality training data for complex CV algorithms.

While many CV models will likely learn effectively when data is labeled using more straightforward bounding boxes, polygon annotation will probably produce a more accurate model when the model is intended to predict complex shapes.

2: Flexibility and Versatility

Polygon annotation is versatile and adaptable to various object types and shapes.

Whether dealing with simple geometric shapes or complex, irregular boundaries, polygon annotation can be used to accurately label objects in images.

3: Handling Occlusion

In real-world scenarios, objects are often partially occluded by other objects or shapes.

Polygon annotation allows annotators to accurately label only the visible parts of occluded objects, which helps machine learning models better understand and handle occlusion in their predictions.

4: Noise Reduction

Polygon annotation cuts out the noise associated with bounding boxes, which include a lot of useless or irrelevant data. This results in cleaner training data and more accurate models.

5: Can Be Automated

Modern labeling platforms semi-automate polygon annotation by automatically finding the boundaries between objects and shapes.

Applications and Uses of Polygon Annotation

Manufacturing: Quality Control and Inspection

Polygon annotation plays a pivotal role in manufacturing, particularly in quality control and inspection:

1. Defect Detection: Identifying defects in manufactured products is essential for maintaining quality standards. Polygon annotation can be employed to label defects in images, enabling machine learning models to identify and classify various defect types automatically.‍
2. Robotic Vision: In manufacturing, robots often require accurate object recognition and localization for tasks such as assembly, pick-and-place, or sorting. In robotics, polygon annotations are used to label hand positions and train robots to grasp objects like humans.

Retail: Enhancing Customer Experience

Polygon annotation is invaluable in the retail industry. Read our article on why data labeling is essential in retail and ecommerce here.

1. Visual Search: Visual search enables customers to search for products using images instead of keywords. Polygon annotation can be used to label products, training machine learning models to recognize and recommend visually similar items.‍
2. Shelf Monitoring: Polygon annotation can be employed to label products in shelf images, allowing machine learning models to monitor shelf conditions and alert store staff about restocking or misplaced items.

Healthcare: Revolutionizing Medical Imaging

Due to its abiltiy to label complex shapes, polygon annotation is instrumental in various medical imaging applications:

1. Tumor Segmentation: In oncology, accurately annotating tumor regions in medical images, such as CT or MRI scans, can improve diagnosis, staging, and treatment planning. Polygon annotation allows for precise delineation of tumor boundaries, enabling machine learning models to identify and quantify tumors effectively.
2. Organ Segmentation: Accurate organ segmentation is essential for various clinical applications, such as surgery planning and radiation therapy. Polygon annotation has been employed to label organs in medical images, helping train machine learning models to segment organs automatically with high precision.‍
3. Retinopathy: Polygon annotation has been utilized in detecting diabetic retinopathy. In this study published in F1000 research, researchers used supervised and unsupervised methods to annotate retinal images to build a retinopathy deep learning model.

   

Autonomous Vehicles: Enhancing Object Detection and Tracking

Polygons are essential for labeling tasks that involve complex real-world objects, Polygon annotation is used for several applications within autonomous vehicle training:

1. Traffic Sign Recognition: Recognizing traffic signs is crucial for autonomous vehicles to navigate safely and comply with traffic regulations. Polygon annotation allows for accurate labeling of traffic signs, enabling machine learning models to detect and interpret them efficiently.
2. Pedestrian Detection: Researchers from Carnegie Mellon University developed a pedestrian detection system that operates at 15 frames a second. The system used the Caltech dataset, which contains about 50,000 labeled pedestrians, though they generated an additional 4 million synthetic samples. Polygon annotation was instrumental in building the model.

Agriculture: Optimizing Crop Management and Analysis

Polygon annotation is excellent for labeling complex biological forms, essential for uses in agriculture and agri-tech:

1. Weed Detection: Accurate weed detection is vital for precision agriculture, enabling targeted herbicide application and reducing costs. Polygon annotation can be utilized to label weeds in images, allowing machine learning algorithms to differentiate between crops and weeds effectively. Read our blog on ML and agri-tech.
2. Plant Disease Identification: Researchers have employed polygon annotation in identifying plant diseases from leaf images, such as in this study published in Frontiers of Plant Science. By annotating disease symptoms, ML models can help farmers detect plant diseases early, enabling prompt treatment and minimizing crop losses. Aya Data built a dataset for a maize disease identification model using images collected and labeled by our team. However, we chose semantic segmentation rather than polygon labeling. Read the case study here.
3. Yield Analysis: Estimating crop yield is crucial for farm management and supply chain planning. By using polygon annotation to label individual fruits or grains in images, machine learning models can be trained to estimate crop yield with superb accuracy.

Summary: What Is Polygon Annotation and How Does it Work?

Polygon annotation is a computer vision technique that outlines objects by connecting a series of points to form a polygon.

This accurately captures the true shape and boundaries of objects, making it ideal for handling complex or irregular shapes.

Polygon annotation is commonly used in object detection, instance segmentation, and suits applications with complex shapes or occlusions, such as medical imaging or autonomous vehicles.

The team at Aya Data are trained in labeling complex images with polygons, among many other computer vision labeling techniques.

Contact us to discuss your next machine learning labeling project.