With the spread of AI, image processing and video analysis have moved into practical use. You may be considering the utilization of AI in your actual business operations.
To perform image analysis, which is used in various fields, you must understand how to perform image annotation. However, even if you are familiar with the term "annotation," many may not fully understand the specific procedures, points to note, and implementation methods.
Therefore, this article explains image annotation techniques, methods, and precautions for tool utilization. It also covers the outsourcing and in-house production of annotation, so by reading this article, you will understand the specific ways to perform image annotation.
If you are considering its use for in-house operations, please use this as a reference.
|
【Table of Contents】 |
Image annotation refers to the work process of creating training data necessary for AI to perform image processing. There are primarily five types of this training data:
|
Object detection (bounding box) is a method that identifies a specific object in an image and encloses it in a rectangular frame. Object detection is widely implemented and used in applications such as autonomous driving and security cameras.
A bounding box not only indicates the presence of an object but also helps in understanding its position and size.
Detection using bounding boxes may not work well if objects partially or completely overlap or if the background is complex. To overcome these challenges, advanced deep learning models are used.
“What is a bounding box? How is it used in YOLO? A thorough explanation of the advantages and disadvantages of object detection methods”
Region extraction (segmentation) is a method that divides an image into multiple regions (segments) and attaches a label to each region. It plays an effective role in medical image analysis and road sign recognition for autonomous vehicles.
Segmentation can accurately capture the contours of an object and analyze its shape and area. Since labeling is performed on a pixel-by-pixel basis, it enables the analysis of complex images that object detection (bounding box) cannot handle.
There are three main types of segmentation:
Keypoint annotation is a method of labeling characteristic positions or points of specific objects in an image. It is used to accurately capture features of an object's shape or structure, and changes in posture.
Keypoint annotation is utilized in pose estimation, which analyzes human joints and skeletons, and face recognition, which detects facial characteristic points such as eyes, nose, and mouth.
It is an annotation technology used in the healthcare and sports industries to help improve posture and form.
Image classification is a method that classifies an entire image into a specific label or class. It understands the content of the image by extracting features within it and assigns it to a pre-set category.
For example, for an image of a dog, it assigns the label "This is an image of a dog." In image classification, high-precision classification can be performed for various images by learning the diverse features included in the dataset.
Image classification is utilized in product defect determination, plant species identification and health status determination, and detecting suspicious scenes in security cameras.
The main method for performing image annotation consists of the following five steps.
|
We will explain each step below.
To start image annotation, you begin by collecting target image data. For example, if assuming use in autonomous driving, images of roads and traffic signs, or images taken from inside a vehicle, would be the target data.
It is important to secure a sufficient quantity and quality of images at the stage of collecting image data. Doing so will ensure the subsequent image annotation work proceeds smoothly and will increase the precision of the completed AI system.
Once you have collected the image data, you need to select the tools to use for image annotation. By choosing tools suitable for the purpose, you can maximize the efficiency and results of the image annotation work.
Annotation tools include free open-source tools and commercial tools, each with different functions and ease of use. While open-source tools are available for free, many have limited functionality. Commercial tools often provide dedicated support and advanced features but incur costs.
Regarding tools used in image annotation, please select the most appropriate one according to the purpose and project scale.
After selecting the annotation tool, you set the annotation rules. This is the task of defining the targets or areas to be labeled within an image, a process that determines the data to be detected through image processing.
For example, you decide what rules to use for annotation, such as pose estimation, face recognition, or object detection. Depending on the purpose, the type and number of points to set will differ. Labeling methods and classification categories are also determined in this step.
The more specific and accurate the settings, the more efficient the annotation work becomes, leading to improved AI model precision. Appropriate annotation rules are necessary according to the project purpose.
Once the points are set, you begin the annotation work. Using the selected annotation tool, appropriate labels are assigned.
Operation methods for annotation work vary by tool, including automatic, semi-automatic, and manual.
To improve the quality and efficiency of annotation, it is necessary to apply unified judgment criteria to ambiguous cases that inevitably arise during work. Therefore, the reality is that complete automation is still difficult.
Furthermore, when handling highly confidential image data, appropriate security measures must be taken. By making preparations up until the implementation of annotation, work can proceed smoothly.
Once the annotation work is complete, the processed data is output and reviewed. Here, annotation data is exported from the tool in an appropriate format. Common formats include COCO, Pascal VOC, and YOLO.
Subsequently, the exported data is reviewed to check for label errors or inaccurate annotations. Review is essential for ensuring and maintaining the accuracy of annotation. Mutual checks by multiple annotators or specialized QA staff will verify the annotation quality.
Finally, data that passes the review is utilized for training the AI model, helping to improve annotation precision.
When implementing image annotation, there are several points to note:
|
By paying attention to these points, you can proceed with image annotation efficiently. We will explain each point below.
When collecting image annotation datasets, please prepare a large amount of highly varied data. By learning from diverse images taken under different environments and conditions, it is possible to improve model versatility and precision.
Since image annotation is a continuous task, ingenuity is required to maintain accuracy over the medium to long term. If annotation accuracy is unstable, it will result in inconsistency in the AI model's recognition precision.
To maintain annotation accuracy, it is necessary to formulate guidelines for performing image processing based on unified criteria within the team. Furthermore, implementing regular reviews and feedback within the team to find and correct annotation errors or inconsistencies early is effective.
Also, by performing cross-checks among multiple annotators, worker bias and errors can be reduced. Through such operation and management, consistency and reliability of annotation data are ensured, allowing AI model performance to be maintained.
When performing image annotation, it is important to perform labeling with consistent requirements and rules. If you change the annotation requirements or rules every time, the detection range and types will also change, and the data will no longer be consistent.
Therefore, it is necessary to set label definitions and rules, and annotate image data based on those conditions. This keeps the annotated data consistent, allowing for high precision in data analysis and model training.
Once the requirements and rules for annotation points have been decided, try to annotate a complete dataset. If errors or points that cannot be detected are found based on those results, it is a good idea to change settings and re-annotate.
A point of concern when performing image annotation is choosing between outsourcing and in-house production. In conclusion, we recommend outsourcing image annotation. Outsourcing image annotation has the following benefits:
A benefit of outsourcing image annotation is that high-quality annotation by professionals with specialized knowledge can be expected. Annotation agency companies utilize their extensive experience and dedicated tools to provide high-precision annotation.
In particular, for specialized annotation such as medical images, expertise is required to perform high-quality annotation.
Outsourcing ensures scalability, allowing for large amounts of image data to be handled. It is possible to implement image annotation even in cases where internal resource constraints prevent handling it. This will reduce the resource burden within the company.
To produce an image annotation system in-house, it is necessary to secure human resources with excellent AI technology skills. If you have human resources who excel at changing annotation criteria and collecting data, you may not need to outsource.
The benefit of in-house production is that since you can manage the annotation work directly, system customization becomes easier. Furthermore, in-house know-how is accumulated, and skills and knowledge regarding annotation are stored within the organization. This also makes it possible to develop resources capable of handling future projects and other AI-related tasks.
However, in Japan, there is a shortage of human resources skilled in the AI field, and it is difficult to secure personnel to construct systems capable of high-quality image annotation. Even if you do it in-house, acquiring AI talent may cost more than outsourcing.
Given the current state of AI talent in Japan, it can be said that image annotation should be outsourced.
Image annotation is the work required to enable AI to perform image and video recognition, and it is indispensable for AI utilization aimed at saving labor and resolving labor shortages.
Without knowing how to perform image annotation, image analysis utilizing AI technology and the use of tools within your company cannot proceed smoothly. Those considering the utilization of image annotation in actual business need to understand the procedures, methods, and points to note.
When outsourcing image annotation, please evaluate agency companies based on various factors and choose one that provides a product suitable for your purpose.