Artificial intelligence (AI) and machine learning (ML) are fundamental concepts(1) in advanced technologies, like robotics, autonomous vehicles, medical imaging, drones, and security systems. Image annotation is the key driver that brings these technologies to life. It’s also an essential component of image recognition in computer vision (CV) models. The technique creates training data for the visual perception model that AI and ML rely on.
The availability of training data for ML is a crucial point in improving AI performance, thus the importance of an accurate image annotation tool. The benefits of image annotation are being realized in many industries. They have impacted the business community, as well as the average consumer. But before we go deeper into the benefits, let us first understand the basics of image annotation.
What is image annotation?
Image annotation is a process of categorizing images using manual or automated ways on a computer system. With the manual method, data labelers use metadata or tags to identify, classify, detect, and segment the data input into an ML or AI model. This classification helps the models recognize images and other things the same way a person would. In the automated method, a computer system automatically allocates metadata through keywords or captions to a digital image.
Image annotations form the training data fed to supervised learning and, therefore, are important components of CV algorithms. In some projects, a single label can convey the complete information of an image, but other applications require many items labeled in a single label of a specific item. High-quality annotations enable the ML model to see and create accurate insights into the world. If the annotations are of low quality, then the ML model will not perform well.
Types of image annotation
ML engineers use several vital forms of image annotation based on algorithms.
- Bounding box annotation: It involves drawing rectangular lines around the corners of an object based on the shape.
- Polygon annotation: This involves creating boundaries with a frame on an image with high precision to identify the object by size and form. It’s commonly used for facial recognition or recognizing things like logo images and street signs.
- Text annotation: Text annotation creates labels in documents to identify sentence structures and phrases, as language is challenging to understand. It makes datasets that train the model to understand the language, emotion, and purpose behind the words.
- Semantic segmentation: This type of annotation groups parts of an image within the same object class and categorizes pixels. Also called picture segmentation, it creates pixel-level prediction.
Benefits of image annotation for artificial intelligence
High-quality image annotations drive the accuracy of CV models used across various industries today. According to Forbes, it’s estimated that the global ML market will reach USD$30.6 billion in 2024 with a CAGR of 43%. Here’s how industries from across the world benefit from image annotation services.
1. Transport
Self-driving automobiles use CV and advanced machine learning algorithms to power them to ensure safety and efficiency. Vehicle manufacturers like Tesla use image annotation to design innovative applications for their autonomous cars. At the same time, countries across the globe are striving to boost digitization by bringing centralized traffic control.
2. Healthcare
CV improves the quality of healthcare due to enhanced accuracy in diagnosis. Through image annotations, CV systems help diagnostic technologies, like MRIs, and CT scans, to focus on problems and patterns, such as hairline fractures and tumors. This technology prevents delays and reduces patient wait times.
3. Agriculture
CV helps in precision agriculture, which combines traditional agriculture with cutting-edge technology to increase productivity, sustainability, and profitability. It makes it possible to improve soil quality, assess plant health, and forecast agricultural output. Modern agriculture uses drones, robotics, and GPS sensors that play a significant role in crop production. The driving force behind these tools and technologies is image annotation.
4. E-commerce and retail
Image annotation enhances customer experience, as it helps them find the right products. With proper annotation, each product on a website will have specific labels and captions. Annotation ensures that the products are placed in the appropriate categories, leading to better search results. When a customer needs to remember the details of a product, they can search for it by simply placing a saved image of a similar product on the search bar.
Conclusion
Image annotation technology is a significant element of modern-day business. It’s one of the interesting areas that ML and AI training covers. Plus, it’s a fast-moving technology; thus, industries must stay on top to keep up with all the new developments. You need to customize your services to match your specific requirements to offer optimum quality control, seamless project management, and reduced costs.
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- Source: Plato Data Intelligence: Platodata.ai
