The application of digital images, videos and deep learning models makes it possible to train computers to interpret and understand the visual world in ways humans do. This is where image annotation steps in. The computer vision (CV) model accuracy is determined by the quality of these annotations whose usage extends beyond the classification of varied objects in an image and recognition of different classes.
To better understand how human-powered AI and ML automation optimizes real-life operations, let’s take a look at common use cases of image annotation. Here are the categories this post will be covering:
- Autonomous driving
- Security and surveillance
- Sports analytics
- Medical imaging
- Retail automation
Image annotation helps create massive datasets to train autonomous driving software for self-driving vehicles.
To guarantee vehicle safety, your algorithm should learn to identify road signs, cycle lanes, traffic lights, objects in the surroundings as potential hazards, optimal weather conditions, and much more. Further implementations of image annotation for autonomous vehicles include the following:
- In-cabin behavior monitoring in advanced driver-assistance systems (ADAS)
- Steering response navigation
- On-road object and dimension detection
- Movement tracking
- LiDAR sensing
The above metrics require a wide range of image annotation solutions and high-quality training datasets to benefit your computer vision (CV) project.
AI-enabled machines are spreading over all industries and agriculture is no exception. Context-driven data labeling helps farmers protect their crops from being damaged through minimized human involvement. Image annotation in agriculture streamlines the following operations:
Keeping your crops and animals in count through drones is not a fantasy anymore. Livestock husbandry has become easier and much less time-consuming because of the prospects of image annotation.
Can machines identify the soil condition in the field? Kudos to the semantic segmentation. Yes, it is possible to generate large sets of data that would later be trained to detect the solid conditions through deep learning.
Crop health monitoring
Insect or fungus-caused infections are already identifiable all thanks to CV. By investing your time in annotation precision, you are investing in your crop’s health.
Plant fructification detection
Image annotation makes it possible to check the crops’ fructification and maturity levels as well. Increased accuracy in annotation will alert the farmer of the right time for harvesting.
Unwanted crop detection
For the machine to be able to detect the weeds and wildflowers hindering plant growth, there is a need for profound investment in agriculture ML. With image annotation and proper ML training, your crops will be less prone to the intrusion of unwanted plants.
Security and surveillance
The mounting demand for security cameras has become a major driving force for the ML industry. Companies are more inclined to safeguard business operations and secure sensitive data to prevent vandalism, theft, and accidents. For that, automating inventory management and surveillance through image processing, albeit labor-intensive, is worth the effort.
These days, image annotation is an imperative parameter for agile security. It assists processes such as crowd detection, night vision even in the darkest of hours, thermal vision, traffic motion, face identification for theft detection, pedestrian tracking, etc. Based on the annotated images, ML engineers train datasets for high-tech video equipment to provide round-the-clock security surveillance for a safer environment.
With advanced tooling, image annotation will allow you to complete projects of different levels of complexity. Video surveillance, however, requires larger volumes and varieties of datasets to achieve the desired precision.
Insurance is one of the sectors that benefit most from built-in AI, despite the common consensus that they are lagging behind. The bottom line is that AI needs to be trained for extreme accuracy to substitute manual damage evaluation, which is possible only with chunks of annotated data featuring vehicle defects. With advanced assessment levels, the ML model can also provide an ultimate prediction of whether or not the part needs a replacement. More progressive models can even calculate the exact cost of the replaced part.
With extensive pattern recognition, insurance will undergo a considerable reduction in response time, improve its customer experience, and save financial and human resources.
Companies opt for robotics solutions because of cost-efficiency, higher productivity, faster performance, and absence of human resources. ML and AI-driven robotics machinery is trained with supervised labeled datasets to perform authentic human operations, which wouldn’t be possible without extensive data annotation.
Image annotation in robotics covers the entire breadth of industries with integrated automation, be it biotech, agriculture, or manufacturing. It is used for delineating the movement of boxes in warehouses, highlighting storage units and packages, and improving overall production yields.
These machines are exposed to giant volumes of data to be able to observe the surrounding environment, detect potential barriers and blockers throughout movement to make sure the objects are dropped at the target destination.
Data labeling and image annotation serve the sports industry in multiple ways, from sports analytics to individualized fitness program detection. In group sports, CV helps with navigation and performance assessment without direct human interaction. AI-driven technology in sports was especially spot-on for COVID-19, helping at-home fitness practitioners stay afloat of the pandemic. CV technology made it possible to design programs for individuals to maintain the desired shape and physical condition for a particular body type.
Or else, take soccer, for instance: the advancement of AI allows for tracking movements of individual players with unmatched precision, which then helps with the assessment and evaluation of the team strategy. Consistent AI observations can even identify patterns in the game and almost encode the opponent’s strategy.
Additionally, coaches and athletes can benefit from image annotation use cases in healthcare to better scan and diagnose injuries and possible illnesses. Quicker detection alerts coaches to act upon the health records on time and propose changes in the team structure and succeeding action plan if need be.
The training of machine learning models is a blessing to the medical industry. The success of the modern healthcare system is credited to the escalating development in AI. Diseases like brain tumors, blood clotting, and a handful of neurological disorders are detected through CT scans and MRI, both of which function based off of well-trained ML models with considerable medical image data.
Other image annotation medical use cases range from quantitative analysis for cancer cell detection to kidney stones, teeth segmentation, eye cell analysis up until microscopic cell analysis on nano levels. Using these datasets, the ML model performs deep learning to develop an automated diagnosis mechanism for the healthcare industry.
These days, you don’t have to wrap your head around convoluted algorithms to find your exact outfit match. Premature data labeling and image annotation have cut you slack, making trendy clothing and accessories recognizable by AI-based tech.
By using available technology you can even receive customized fashion analytics with trend forecasting to make sure you are producing or purchasing the right design at the right time.
Here is the list of concrete use cases tailored to your fashion needs:
- Semantic segmentation to outline a set of clothes
- Tagging fashionable items
- Visual search for clothing
- Visual search for accessories and other wearables
As a customer, you can find and shop anything you take a snap of. With proper training, vision-based technology will quickly recognize fashion items and connect you with the respective retail store — a shopaholic’s dream. Yet, fashion-based image annotation is a win-win in that it expands the customer flow giving shop owners a competitive advantage.
The use of CV models in retail couldn’t be more promising and for a good reason. AI spendings are estimated to exceed $12 billion by 2023, as Jupiter Research mentions. In retail, image annotation is all about creating novel dimensions for e-commerce and other subdivisions. It has become an indispensable part of the retail industry to provide a high-quality shopping experience.
The use cases for annotations vary from virtual inventory management to shopping counts, human counts, and time spent on a product, up until interested object interactions. Due to this spectrum of functionalities, AI has become more and more integrated into the stores’ merchandising efforts. With that in mind, CV implementation in department stores has revolutionized the shopping culture with its bold attempt to match the virtual product to the actual, continuously making predictions on the customer behavior.
The escalating shift towards digitalization worldwide emphasizes the need for deep learning models and machines’ ability to detect and classify objects. Image annotation is the nexus of your best computer vision results, regardless of the industry.
We hope this article provides an all-around overview of image annotation use cases to help you make informed decisions and take your ML operations to the next level. As long as dataset labeling is tedious and time-consuming, it might be a smart decision to outsource the burden of meticulous annotation to experts or groups of experts, depending on the data volume. Feel free to reach out should you need more information on the marketplace of annotation services and comprehensive training for your computer vision model.