Image annotation
#Inveniam’s Guide to Image Annotation for Accurate Computer Vision Models
PUBLISHED ON 07/02/2024
Image annotation is a crucial element of computer vision that allows machine learning algorithms to process visual data. Compared to the advancements in machine learning, annotation can sometimes get overlooked. However, without the proper annotation processes in place, it’s not just a matter of losing the accuracy of your AI models – we’re talking about the success of an entire project or product.
In this article, we’ll explain what image annotation is, how we approach this process, why we choose to have an in-house team of annotators and how that impacts the success and speed of our solutions.
Table of contents
- What’s image annotation?
- The role of image annotation in computer vision
- Image annotation process
- In-house annotation team vs outsourcing
- How Inveniam ensures high accuracy
What’s image annotation?
To facilitate a machine's understanding of visual data, human annotators use specific tools to label or highlight relevant elements in images. This process, known as image annotation, generates metadata that not only train machine learning models to recognise and interpret visual elements but also serves broader purposes like detecting errors in images.
Beyond enabling machine learning models, this metadata plays a key role in data analysis and extracting insights, enhancing our understanding of the dataset's diversity and real-world applicability. The ultimate of image annotation is to train machine learning or deep learning models so they replicate the annotation process autonomously.
The process of image annotation highly depends on the type of data being annotated and the specific requirements of the AI model being developed. For example, bounding box annotation (a type of annotation) draws a box around an object in an image and assigns it a label. In contrast, semantic segmentation labels each pixel in an image to provide a detailed understanding of the scene.
Image annotation techniques
As mentioned before, there are different types of image annotation. Specific annotation tasks necessitate data to be annotated in different forms so that the processed data can be used directly for training. The types of image annotation shapes include:
- Bounding box – can be either two-dimensional (2D) or three-dimensional (3D, also called 3D cuboid in annotation), used to outline simple shapes like road signs, vehicles, and other symmetrical boxes.
- Polygon – used to outline irregular objects or outline their boundaries using a series of connected vertices. Instead of using rectangles (bounding boxes) to define the extent of an object, polygon annotation allows for more precise delineation of complex shapes.
- Semantic segmentation – used for dividing an image into segments or regions and assigning a specific class label to each pixel within those segments. This technique is considered the most precise form of annotation.
- Polyline – used to outline or trace the shape of an object or feature within an image using a series of connected line segments. Unlike polygon annotation, polyline annotation consists of open shapes made up of straight-line segments.
- Keypoint or landmark – used to identify and label specific points on objects within an image. These key points, also known as landmarks, serve as reference locations that are crucial for understanding the structure, orientation, or characteristics of the objects. Key points are typically annotated with coordinates.
- Lines – used to indicate features, boundaries, or patterns that can be represented by straight or curved lines. This method is less common than some other annotation types, but it can be useful in certain applications.
- Markers – used for placing markers or points at specific locations within an image to highlight key features, points of interest, or other relevant information. Markers are typically singular points with no connecting lines.
Image annotation types
Visual data can be extremely varied, portraying different objects, landscapes, or people. To help correctly process images, data scientists must choose the proper annotation type before moving forward. Whether it’s singling out what needs to be analysed in each photo, annotators must carefully annotate large volumes of visual data to ensure accurate results.
Image classification or recognition is a process of assigning a single label or tag to an image together with a description. Typically, supervised deep learning algorithms are used for image classification tasks and are trained on images annotated with a label chosen from a fixed set of predefined labels. This allows machines to recognise objects in images and across an entire dataset.
Object detection focuses on detecting specific objects in an image. The annotations for these tasks are in the form of bounding boxes and class names, where the coordinates of the bounding boxes and the class ID are set as the ground truth. Object recognition (or detection) helps machines identify and label specific objects in an image, including their instances, locations, and numbers.
In short, image classification focuses on assigning a single label to an entire image, object detection goes further by identifying and localising multiple objects within the image.
The role of image annotation in computer vision
Image annotation provides the 'training ground' for artificial intelligence (AI) models, teaching them how to 'see' and understand images, just like humans do. Image annotation is used in a wide range of applications, from autonomous driving to medical imaging and agriculture technology, facial recognition systems, to medical imaging analysis.
Annotation is fundamental for leveraging the capabilities of computer vision. It enables the development of models and applications that enhance network performance, RFT installations, maintenance, and overall customer experience. Telco companies can use computer vision to address key challenges like expensive rework, network roll-out delays, and quality control by accurately labelling and annotating visual data.
Image annotation process
Typically, the process is similar for every annotation team. However, it can vary depending on the purpose. In this section, we’ll review and explain how Inveniam’s in-house annotator team approaches the entire process in 6 steps. But before we dive into that, it’s important to take a step back and explain how our data science and annotation teams work on a high-level scope when building and training a new AI model.
We use a combination of CRISP-DM (Cross-Industry Standard Process for Data Mining) and Agile-Scrum methodology. CRISP-DM is a widely used methodology for guiding data mining and machine learning projects. It provides a structured and comprehensive framework to ensure that data mining projects are conducted in a systematic and repeatable manner, allowing organisations to effectively leverage their data for actionable insights.
Agile-Scrum is a popular project management framework used to develop software and manage complex projects. It’s part of the larger Agile methodology, emphasising iterative and incremental development, collaboration, and flexibility in responding to changing requirements. Scrum provides a structured framework for teams to work together and deliver valuable products in a collaborative manner.
By using the combination of these two methodologies, our team can ensure we always emphasise and prioritise our client’s business needs and goals. This means that we allocate sufficient time at the beginning of a project to develop the best development plan. At the same time, together with the adaptability mode, we make sure to structure and organise progress so that all clients are happy with the service at the end of the project.
Image annotation summarised in 6 steps
Now that we’ve established how we plan new projects and what methods we use to support them, here’s a step-by-step walkthrough of how we approach image annotation.
Step 1: Outlining key objectives – after a thorough review and research of a client's business needs, our data science team provides specific and detailed project objectives and guidelines to the annotation team.
Step 2: Preparing datasets – the data scientist team gathers visual data to organise a dataset required for annotation properly. Once that’s done, the DS team provides annotation instructions to the annotation team. To meet the specific project requirements, the visual data must be relevant and diverse to ensure the best end results.
Step 3: Setting up a project – we use special annotation tools to configure an annotation project. This also involves defining tags, attributes, and settings.
Step 4: Defining, delegating and tracking tasks – once the project is properly set up, we break down the annotation workload and create specific annotation tasks based on the prepared dataset and project requirements. We utilise task-tracking platforms to help track progress and make sure the process is proceeding as planned.
Step 5: Communicating throughout the project – communication and collaboration via designated communication channels ensure consistency and resolve uncertainties during annotation, which has a direct impact on the success of the annotation project.
Step 6: Quality assurance – in the last and the most crucial stage of the entire annotation process, we conduct a meticulous review of completed annotations to ensure accuracy and adherence to project guidelines. This allows us to address any discrepancies or errors found during the quality check process.
In-house annotation team vs outsourcing
Computer vision and other AI solutions face the decision of whether to establish an in-house annotation team or to outsource this task to external annotation services. Both approaches have advantages and challenges, and the choice often depends on the organisation's specific needs, resources, and priorities.
In-house image annotation team
Benefits
- Control and security – computer vision solutions can maintain confidentiality, security, and compliance with data protection regulations more effectively.
- Immediate communication – direct communication between the annotation team data scientists or engineers leads to faster feedback loops and better end results.
- High-level domain knowledge – an in-house team can develop domain-specific knowledge about the company's projects, products, and specific requirements. This has a direct impact on the speed and accuracy of AI models and projects.
- Customisation and adaptability – being able to offer custom-tailored solutions for the different, often unique needs of clients can offer a competing edge in terms of customer experience and product quality.
- Long-term cost control – while setting up an in-house team involves initial costs, it may be more cost-effective in the long run, especially for organizations with ongoing and predictable annotation needs.
Challenges
- Resource management – managing human resources, including hiring and training annotators, can be a continuous challenge.
- Scalability – Scaling the in-house team to handle large volumes of data or sudden increases in workload may pose challenges in terms of resources and time.
Outsourcing image annotation
Benefits
- Cost savings – outsourcing can often be more cost-effective, especially for short-term or fluctuating annotation needs.
- Scalability – outsourcing provides the flexibility to scale up or down quickly in response to changing annotation requirements. This is particularly beneficial for projects with varying workloads.
- Focus on core competencies – companies can concentrate on their core business activities while leaving the annotation tasks to specialised service providers.
Challenges
- Communication and coordination – managing communication between the external annotation team and internal stakeholders can be challenging, particularly when dealing with different clients and their varying needs.
- Data security concerns – companies must select reputable annotation services to avoid data breaches and sharing with third parties.
- Dependency on external providers – companies become reliant on the performance and reliability of external annotation providers. Issues with quality or delays in delivery can impact project timelines.
- Potential lack of domain knowledge – annotation service providers may provide specific knowledge in a single area, but in terms of knowledge depth and diversity, they may not have the same level of experience as an in-house team.
The decision between an in-house image annotation team and outsourcing depends on factors like the nature and scale of the annotation task, as well as the company's budget constraints. However, we’ve found that having an in-house annotation team is what helps us differentiate and maximise the quality and accuracy of our computer vision solutions.
How Inveniam ensures high accuracy
Thanks to our team of dedicated data scientists and annotators, Inveniam ensures leading results in terms of accuracy and quality. After partnering up with Axione, we were able to achieve great results together. More specifically, we helped drive a 46% reduction in incident tickets, analysing more than 2M images in the last year.
Our computer vision solution is completely managed by us, meaning that we provide everything from management to maintenance and data processing. Moreover, having an in-house annotator team gives us a unique advantage in terms of not just training AI models for clients, but adapting to their changing business needs in a demanding market.
Closing thoughts
Telecommunications companies empower their computer vision systems to recognise and analyse key elements across fixed and mobile infrastructure and network components by accurately labelling and annotating visual data. This helps to facilitate precise decision-making, predictive maintenance, and the seamless integration of innovative technologies.
As the telecommunications industry continues to evolve, image annotation remains an invaluable tool, providing the foundation for harnessing the full potential of computer vision to enhance operational efficiency, optimise network performance, and deliver superior services to end-users.