Machine learning (ML) and artificial intelligence (AI) have brought revolutionary changes in various industries worldwide. The machines and applications used daily have become more intelligent than anyone could imagine. Advanced technologies like drones, robotics, security systems, autonomous vehicles, and medical imaging heavily rely on ML. The key driver behind these remarkable technologies is image annotation.
Image annotation, such as bounding boxes, is crucial for developing ML and AI in many sectors and fields. Estimates based on the Global Annotation Tools report indicate that ML and AI data preparation market will reach USD$3.4 billion by 2028.
This data shows that the need for image annotation services is growing every day in many companies across industries. There’s an increase in the need for companies to prepare more data for training the ML and AI models to provide more accurate predictions.
Developing an AI model is an ambitious project, so one must have access to high-quality data. Investing in an image annotation tool platform that uses quality images to train, test algorithms, and validate data will make your AI project successful.
Labeling each image carefully and precisely in a dataset will train your AI system to identify objects just like a human being would. Accurate image annotations lead to better performance of ML, so a lot depends on the labeling phase.
What is image annotation?
Image annotation is the process of labeling a particular dataset of images to help train an ML model. The computer automatically assigns metadata as captions or keywords to a digital image. An AI engineer can also manually set the labels that provide details about the image to a computer vision model. While a single label can sufficiently convey complete image information in specific projects, the number of labels on a single image might differ. Some applications require various items labeled with distinct labels in a single image.
How image annotation benefits businesses
The volume and variety of image data in the business world are increasing daily. Organizations in different sectors are benefiting from image annotation services. Here are some examples.
Improves the quality of healthcare services
The healthcare sector uses image annotation to empower deep learning models that enhance the precision of diagnosis, thus improving the quality of treatment. For instance, computer vision models with precisely annotated data can recognize images from MRI, X-ray, and CT scans. They can interpret patterns and point out tumors, abscesses, hairline fractures, cancers, and more.
Enhances security and surveillance
Image annotation is exceptionally useful in the security and surveillance industry. With security cameras everywhere today, companies invest heavily in surveillance equipment to avoid vandalism, theft, and accidents. The ML engineers train computer vision models in face detection, pedestrian tracking, traffic monitoring, crowd detection, and night vision.
These datasets can identify demographics and estimate the number of people besides tracking a person in a crowd. The models use annotated photos to train datasets in video and surveillance equipment, thus providing a more secure environment.
Aids transport design
Self-driving cars are common today, using computer vision models for their autonomous operation; sophisticated ML algorithms power these vehicles to run efficiently and ensure safety. Automobile manufacturers use image annotation to design innovative applications for vehicles like Tesla and autonomous taxis like Uber. There’s also a thrust by government sectors to boost digitization that will bring seamless and centralized traffic control globally.
Boosts agriculture
Image annotation has transformed the agricultural sector by blending precision farming using the latest technology with traditional methods to boost productivity, sustainability, and profit. Computer vision models can analyze plant health, enhance soil conditions, and project crop yields. They can identify unwanted crops, insects, wildflowers, and weeds and take appropriate action. Drones, robots, and GPS sensors play a significant role in agriculture today, and image annotation is behind these tools and technologies.
Improves eCommerce and retail
Image annotation dramatically enhances the user experience in eCommerce as it helps customers to find the right products. Correctly done annotation has specific labels and captions for individual items on a website. Annotation helps to bring up better search results due to the appropriate categorization of products. It boosts visual search whereby customers can use a saved image in the search bar to find a product when they don’t know its details. Offline retailers benefit from image annotation in inventory management and cashier-less checkout systems.
Final Thoughts
Image annotation is becoming inevitable and is a great way to get the best out of technology. It’s crucial in enhancing healthcare services, boosting agricultural initiatives, and preventing crimes. Furthermore, it improves eCommerce processes and is even beneficial to the transport industry.
