AI-based image recognition continues to advance relentlessly in search of higher precision. Panoptic segmentation is one of the segmentation methods that serves as a vital technology for image recognition AI.
Like many new technologies, panoptic segmentation has foundational techniques and methods. By understanding the relationship with the underlying technologies, the characteristics, strengths, and applications of this new technology become clearer.
In this article, we provide an easy-to-understand explanation of the relationship between panoptic segmentation and its foundations: semantic segmentation and instance segmentation. We have also summarized use cases, making it an article that helps you visualize how to use panoptic segmentation after its introduction. Please use this as a reference.
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Panoptic segmentation is a method that simultaneously assigns class information and individual object information to every pixel within an image. It can be called an advanced method in image recognition that combines the strengths of semantic segmentation and instance segmentation.
Unlike traditional segmentation, it is possible to recognize each object individually while simultaneously understanding positional relationships and shapes within the image in detail. Furthermore, it can handle both countable objects and background elements identified by material or texture.
It enables advanced analysis and judgment in a wide range of application fields such as autonomous driving, surveillance systems, and medical image analysis.
In panoptic segmentation, semantic segmentation identifies which class (e.g., sky, road, building, etc.) each pixel belongs to. Simultaneously, instance segmentation identifies individual objects and attaches a label to each.
By integrating these two methods, it becomes possible to identify individual objects while labeling every pixel in the image. In this mechanism, deep learning models such as Convolutional Neural Networks (CNN) and Transformers play a central role.
We will explain the differences between the panoptic segmentation method and the following segmentation methods:
Semantic segmentation is a method that assigns a class to each pixel within an image. It can identify which parts of the entire image correspond to which class.
However, semantic segmentation cannot distinguish between individual objects within the same class. For example, even if multiple cars exist in an image, they are all treated as a single "car" group.
In contrast, panoptic segmentation identifies individual object instances within the same class in addition to class-based identification. In other words, if there are multiple cars, a unique label is attached to each.
Furthermore, "What is semantic segmentation? Explaining types, methods, and image processing application examples!" provides a detailed explanation of the mechanism and use cases of semantic segmentation.
Instance segmentation is a method that identifies each object in an image individually and specifies its outline and position. It can be called a method shifted toward the recognition of objects within an image. Therefore, instance segmentation does not provide detailed information about the background or non-object areas.
On the other hand, panoptic segmentation assigns category information to every pixel in the image. In other words, it can comprehensively analyze the entire image, including background and environmental elements.
"What is instance segmentation? A thorough explanation of the differences from semantics, representative models, methods, and advantages!" provides a detailed explanation of the mechanism and fields of application for instance segmentation.
In panoptic segmentation, the following AI technologies are mainly applied:
We will explain each of these.
Dilated convolution is a convolution method that plays an important role in segmentation in general, including panoptic segmentation. It can capture a wider range of information than normal convolution, making it easier to understand the overall shape of objects and the surrounding context.
By utilizing dilated convolution, a model can learn relationships between distant pixels while maintaining a high-resolution feature map. It is possible to accurately capture object outlines and boundaries with the background, retaining both fine details and global information.
An attention mechanism is a technology where deep learning models selectively focus on more important parts of the input data.
In panoptic segmentation, multiple attention mechanisms are used in parallel. This allows for effective processing of both "things" (individual objects) and "stuff" (background and materials).
GANs play a role in panoptic segmentation by improving data quality and diversity, and supporting model learning and adaptation.
A Generative Adversarial Network (GAN) consists of two networks—a generator and a discriminator—where the generator creates image data similar to the real thing and the discriminator determines if that image data is real or fake, thereby improving generative capabilities.
In panoptic segmentation, GANs are used to extend existing image datasets. In particular, they can generate more diverse scenes containing both "things" (individual objects) and "stuff" (background and materials).
Active Contour Models (ACM) are a method for accurately capturing the outlines of objects within an image. This model smoothly tracks object boundaries based on the principle of energy minimization.
Meanwhile, CNNs automatically extract features from images to achieve advanced pattern recognition.
By utilizing the semantic information extracted by the CNN, the active contour model precisely detects the detailed boundaries of objects. By combining these two technologies, it is possible to significantly improve the precision and efficiency of panoptic segmentation.
This integrated method is particularly effective in fields where detail recognition is required, such as medical image analysis and autonomous driving. For example, in the medical field, the precision of diagnosis and treatment planning can be enhanced by detecting accurate boundaries of tumors or lesion sites.
Due to its high image understanding capability, panoptic segmentation is utilized in various scenes. Among them, the following three are areas where its introduction is progressing:
We will explain each use case.
Autonomous vehicles require technology to accurately understand the surrounding environment in real-time to achieve safe and efficient driving. To realize this advanced environmental recognition, the utilization of panoptic segmentation is expected.
By applying panoptic segmentation to image data obtained from vehicle cameras and sensors, all elements—such as roads, pedestrians, other vehicles, traffic lights, and signs—can be classified at the pixel level. Simultaneously, each object is identified as an individual instance.
Panoptic segmentation is also used in the field of medical image analysis. In medical settings, a wide variety of image data such as X-ray, CT, MRI, and ultrasound are utilized for diagnosis and treatment planning. Accurately identifying organs and lesion sites from these images is important for improving patient clinical quality and treatment effects.
By introducing panoptic segmentation, it becomes possible to analyze not just the whole organ, but also individual lesions and tissue structures existing inside it in detail at the pixel level.
Through panoptic segmentation, robots can analyze surrounding objects and backgrounds, enabling more advanced judgment and actions. When operating robots in the real world, they work in diverse environments as replacements for or aids to humans. Therefore, the ability to accurately and quickly recognize and understand the surrounding environment is required.
Panoptic segmentation enables the distinction of boundaries with the background and multiple objects within the same class. It then assigns classes and individual labels to every pixel in the image. This allows the robot to understand the surrounding situation more comprehensively.
High-quality annotation is indispensable to achieve panoptic segmentation with the precision required for your company's needs.
Panoptic segmentation combines elements of both semantic segmentation and instance segmentation. Therefore, in annotation work, both class labels and instance IDs must be accurately attached to every object within the training images.
Specifically, a multi-stage annotation process like the following may be configured:
Because the work is performed in stages, annotation for panoptic segmentation is more complex and time-consuming. It is necessary to build a quality control process that can clearly control each stage.
To make the annotation process go smoothly, consider using a dedicated annotation tool. In addition, incorporating cross-checks by multiple annotators and verification by experts into the flow is also required.
Accurate annotation is vital to the success of panoptic segmentation. By understanding the importance of annotation, you will be able to operate high-precision panoptic segmentation.
Please also see "What is annotation? Why is it necessary for AI use? Explaining the process and work involved".
Panoptic segmentation is a segmentation method that enables class and individual image recognition, achieving high-precision analysis. For this, advanced annotation is required, but it also carries a large workload as it requires specialized knowledge and technology.
In order to reflect the precision of annotation in the AI model, it is important to maintain consistency and accuracy in labeling. If low-precision annotation is performed, the precision of panoptic segmentation will also decrease.
If there are no annotation personnel within your company, please consider consulting an annotation specialist company.