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Use cases

Learn how artificial intelligence is augmenting or automating human decision making around the world. Labelbox is the foundational infrastructure that powers all these applications within businesses big and small.

Ultrasonography

Ultrasonography is the primary imaging modality for identifying and characterizing ovarian masses. Companies use Labelbox to build computer vision to differentiate benign and malignant ovarian masses.

Driver safety

Dual-facing dash cams are used by the trucking industry to record critical events such as collisions. Companies use Labelbox to build models that identify important patterns such as driver distraction and causes of front-facing collisions.

Crop weed detection

Selective weed treatment is a critical step in autonomous crop management as related to crop health and yield. However, a key challenge is reliable, and accurate weed detection to minimize damage to surrounding plants.

Property inspection

Roof and overall home outdoor inspection is slow and costly. Companies use Labelbox to build computer vision to understand the quality of structure about any address on the planet.

Solar inspection

Solar inspections are a time and labor intensive process. AI-powered drones are being used to inspect solar farms and can fly pre-planned routes capturing thermal images along with geospatial metadata, generating actionable reports.

Content moderation

For ecommerce and social media companies where consumer trust and experience are paramount, deep learning techniques can be used to accurately and quickly moderate content at scale, whether it’s detecting potential offensive and unwanted images or filtering profanity and undesirable text.

Sports analytics

Traditional sports and esports are utilizing computer vision to understand and classify every action without any human intervention. Labelbox is used by various teams for real-time and batch processing training data pipeline.

Thermal sensing

Thermal cameras are powerful sensors that can "see" objects in a different spectrum that human eye can. Labelbox is being used to build state of the art computer vision intelligence that make thermal sensors smart.

Digital pathology

Digital pathology is undergoing a revolutionary change with the introduction of deep learning based pattern recognition. Labelbox supports rendering histology slides that enables some of the largest companies to easily create high quality training data with domain experts.

Livestock monitoring

Understanding animal health and environmental changes is critical to sustaining a healthy production line on a dairy farm. Companies use Labelbox to help analyze data from surveillance camera footage in order to provide the animals with the health and care needed. This makes the animals happier and allows the farms to be more productive.

Preventative maintenance

Companies are harnessing AI-powered sensors to monitor data on machinery and components in order to collect data points, identify signals and take corrective actions before assets break down.

Defect detection

In mass production, checking whether every product is built to specification is a repetitive job that is limited by human fallibility. Factories are employing machine learning to scan for imperfections that the human eye might miss while cameras use AI to categorize boxes in warehouses and scan for defects during the QA phase.

Safety monitoring

For construction safety and health, the continuous monitoring of unsafe conditions is essential in order to eliminate potential hazards in a timely manner. Computer vision is being applied for the extraction of safety related information from site images and videos, to complement current time-consuming and unreliable manual observational practices.

Robotics automation

Collaborative robots are being trained to see 2D and 3D environments which enables them to make more accurate manual movements that detect, map, and predict their trajectories quickly. These robots work alongside humans to handle potentially dangerous elements of the job and are being used in areas such as smart self-driving forklifts or conveyors that move materials and finished goods around.

Waste management

Managing waste has traditionally been a largely manual process. AI is now being used to develop waste recognition software that can monitor, audit and sort waste at scale through the use of autonomous robots, intelligent trash bins and waste stream sorting.

Document data extraction

Large financial services firms increasingly rely on computer vision to automate document data extraction. Businesses can save time and costs in assessing large numbers of incoming paper documents and augment traditional processes such as manual data entry or optical character recognition technology which can be tedious and error-prone.

Cashierless checkout

Retail stores are embracing fast and convenient in-store checkout experiences to reduce transaction times and operational costs. Through the use of sensors, cameras, scanners, and mobile devices, the in-store checkout experience is being transformed by leveraging computer vision to detect and count products on the checkout counter in real-time.

Generative design

Consumer tastes have broadened, and manufacturers are trying to keep up with increasing demands for customization and variety. ML can be used for rapid prototyping and assembling a specific series of products while iterating on those products based on consumer feedback and predictive information.

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